Understanding Entities in Dialogflow
Summary
TLDRIn this episode of 'Deconstructing Chatbots,' Priyanka Vergadia delves into Dialogflow's entities, which are instrumental in extracting data from user inputs. She distinguishes between system entities, developer entities, and session entities, using an appointment scheduler as an example. The tutorial guides viewers on creating entities for DMV services, integrating them into intents, and utilizing slot filling to ensure all necessary information is gathered. Priyanka also introduces session entities, which are unique to each user session and can be dynamically updated. The episode concludes with a teaser for the next installment, which will focus on integrating the scheduler with Twilio Messaging Service.
Takeaways
- 🤖 Entities are Dialogflow's way of identifying and extracting data from user input.
- 📚 There are three types of entities: system entities, developer entities, and session entities.
- 📅 System entities automatically extract common data like dates, times, addresses, emails, currency, and phone numbers.
- 🔍 Developer entities are custom entities created by developers for specific use cases, like 'Appointment Type' for a DMV scheduler.
- 📝 Session entities are tied to a specific user session and can be updated dynamically based on the conversation.
- 🛠️ You can create entities for specific services like 'drivers license' and 'vehicle registration' to tailor your chatbot's understanding.
- 📑 Dialogflow allows uploading or downloading entities in bulk using CSV or JSON formats for easier management.
- 🔗 Adding intents and training phrases helps the chatbot understand and respond to user inputs involving the new entities.
- 🗂️ Slot filling ensures that all necessary information (like appointment type, time, and date) is collected before proceeding.
- 📝 Modifying responses to include all relevant information (appointment type, date, and time) makes the chatbot's output more informative.
- 🔄 Always save changes after making updates to intents, entities, or responses to ensure they are implemented correctly.
Q & A
What is the main focus of the episode 'Deconstructing Chatbots'?
-The main focus of the episode is to explore entities in Dialogflow, which are used for identifying and extracting useful data from user input.
What are the three types of entities mentioned in the script?
-The three types of entities mentioned are system entities, developer entities, and session entities.
How are system entities beneficial for Dialogflow agents?
-System entities are beneficial as they allow agents to extract information about a wide range of concepts without any additional configuration.
Can you provide an example of a system entity?
-Examples of system entities include data like addresses, emails, currency, and phone numbers.
What is a developer entity and how is it created?
-A developer entity is a custom entity created by the developer to capture specific types of information, such as 'drivers license' and 'vehicle registration' in the context of a DMV appointment scheduler.
How does Dialogflow handle entities that are not initially listed?
-Dialogflow can automatically identify and add new entities to the list based on other training phrases if the checkbox is checked.
What is the purpose of slot filling mentioned in the script?
-Slot filling is used to ensure that all required information, such as appointment type, time, and date, is collected from the user to book an accurate appointment.
How can entities be managed in bulk?
-Entities can be managed in bulk by uploading or downloading them in CSV or JSON format.
What is a session entity and how does it differ from other entities?
-A session entity is defined at the session ID level and is tied to a specific user and their session, allowing for dynamic updates based on the conversation.
How does the chatbot respond when a user provides all the required information in one input?
-When a user provides all required information in one input, the chatbot sends a confirmation response back to the user right away.
What is the next step for the appointment scheduler chatbot discussed in the script?
-The next step is to integrate the appointment scheduler with Twilio Messaging Service, which will be covered in the next episode.
Outlines
🤖 Introduction to Entities in Dialogflow
Priyanka Vergadia hosts 'Deconstructing Chatbots', where she delves into Dialogflow's entities. Entities are a mechanism for extracting data from user input. She reminds viewers to watch previous episodes for context and explains the three types of entities: system, developer, and session. System entities automatically extract common data like addresses and phone numbers. Developer entities are customized for specific use cases, demonstrated by creating an 'Appointment Type' for a DMV scheduler, including services like driver's license and vehicle registration. The episode also covers adding entities, using slot filling to ensure all necessary information is collected, and modifying responses to include appointment details. The importance of saving changes and testing updates is emphasized.
📚 Session Entities and Upcoming Integration
The second paragraph focuses on session entities, which are unique to each user session and can be updated dynamically based on the conversation. Priyanka gives an example of how an 'Appointment Type' entity might be updated to include a state ID or motorcycle test. She encourages viewers to try these concepts and share their experiences. The episode concludes with a teaser for the next installment, which will cover integrating the appointment scheduler with Twilio Messaging Service.
Mindmap
Keywords
💡Entities
💡System Entities
💡Developer Entities
💡Session Entities
💡Intents
💡Slot Filling
💡Training Phrases
💡Prompts
💡API
💡Dialogflow
💡Twilio Messaging Service
Highlights
Introduction to the episode 'Deconstructing Chatbots' with Priyanka Vergadia
Exploration of entities as Dialogflow's mechanism for extracting data
Explanation of the three types of entities: system, developer, and session entities
Demonstration of how system entities automatically extract information like date, time, address, emails, etc.
Example of creating a developer entity 'Appointment Type' for a DMV appointment scheduler
Discussion on adding entity types like 'drivers license' and 'vehicle registration'
Option to automatically add new entities based on training phrases
Capability to upload or download entities in bulk formats like CSV or JSON
Adding intents to accommodate the 'appointment type' entity in user inputs
Slot filling as a method to ensure all required fields are provided by the user
Defining prompts for when users do not provide the appointment type
Modifying responses to include appointment type along with date and time
Testing updates with sample inputs to ensure the chatbot works correctly
Explanation of session entities and their use tied to a specific user session
Example of updating session entities based on conversation with the user
Invitation to try the methods discussed and share feedback in the comments
Summary of the episode covering the three types of entities and their setup in a chatbot
Teaser for the next episode which will integrate the appointment scheduler with Twilio Messaging Service
Transcripts
PRIYANKA VERGADIA: Hey, everyone.
I am Priyanka Vergadia, and you are watching
"Deconstructing Chatbots."
In today's episode, we will explore entities further.
[MUSIC PLAYING]
From the second and third episodes,
you might remember that entities are Dialogflow's mechanism
for identifying and extracting useful data from user's input.
If you've not checked out the episode
on "Getting Started with Dialogflow,"
please do so for more context.
Just scroll up to the second episode on the right,
or click in the link and description below.
There are three types of entities-- system entities,
developer entities, and session entities.
Now you might remember from our appointment scheduler chatbot
when we said need an appointment for 4:00 PM tomorrow,
the date and time were automatically
extracted as system entities date and time.
It was all automatic.
As you saw, system entities allow
agents to extract information about a wide range of concepts
without any additional configuration.
Data like address, emails, currency, phone numbers
are some of the common examples of system entities.
Now so far, we have been addressing our appointment
scheduler in a generic manner.
Let's say our scheduler is for DMV.
We click on Entities and create a new entity.
Let's call this Appointment Type.
Let's think about the types of services DMV offers.
Most people go to the DMV for a drivers license,
maybe vehicle registration, also driving tests.
So in our scenario, let's stick to the two main services--
drivers license and vehicle registration
are obviously the two main ones that I
can think of that DMV offers.
So let's create those two as entities.
You can add more rows with more services as entity types.
Additionally, you can check the box
to add more entities automatically.
When an entity is not available in the entity list,
Dialogflow identifies that this is an entity based
on other training phrases and adds that new entity
automatically to the list.
You can also upload or download entities in bulk
in CSV or JSON format.
Now let's add more intents to accommodate the appointment
type entity in our inputs.
When you add Set an Appointment for Driver's License for 3:00
PM tomorrow, driver's license is identified as developer entity
that we just set up, while time and date are obviously
system entities.
Add a few more training phrases like these.
Now remember slot filling from episode three
when we built our appointment scheduler?
To book an accurate appointment, we
needed to make appointment type a required field
just like time and date.
Let's define the right prompt for when a user does not
provide us the appointment type.
Now while we are at it, let's modify our response
to include the appointment type along with the date and time
to make it look more complete.
When done, don't forget to save your changes.
And then, let's test our updates now with some sample inputs.
The first example, let's say, user only
gives us the appointment type.
Well, slot filling kicks in and asks for the time and the date
when they want to come in.
All looks good.
We get the appointment scheduled.
In the second example, let's say, a user says,
I need an appointment for 4:00 PM tomorrow
without providing us the appointment type.
Again, our slot filling kicks in and prompts the user
to provide us the service information.
They say driver's license, and we
provide them a response with their appointment confirmation.
In our third and last example, let's say
the user gave us all three things
we needed in just one shot-- set an appointment for vehicle
registration at 2:00 PM tomorrow.
Our bot has everything it needs, so it
sends a confirmation response back to the user right away.
Now the third type of entity is session entity.
These are defined at session ID level, which
allows for a specific entity to be tied
to a user and their session.
Let's take an example to understand this better.
Our agent had appointment type as entity,
which includes drivers license and vehicle
registration at this point.
That entity could be updated to include a state
ID or a motorcycle test depending
on the information your agent collects from the user
during the conversation.
The updated session entity will have state ID or motorcycle
test entry for rest of the conversation with your user.
Well, try this out on your own, and let
me know how it goes in the comments below.
Remember, you will create, manage, and update
session entities using the API.
That was a lot, so let's summarize.
Today, we looked at the three types of entities--
system entity, developer entity, and session entities.
Then, we went hands on and learned
how to set up these entities in our appointment scheduler
chatbot.
Don't miss the next episode of "Deconstructing Chatbots"
because we are going to integrate our appointment
scheduler with Twilio Messaging Service.
[MUSIC PLAYING]
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