If you’ve been keeping up with the current advancements in the world of chat and voice bots, you’ve probably come across Google’s newest acquisition - DialogFlow (formerly, api.ai) - a platform that provides a use-case specific, engaging voice and text-based conversations, powered by AI. While understanding the intricacies of human conversations, where we say one thing but mean the other, is still an art lost on machines, a domain-specific bot is the closest thing we can build.
What is DialogFlow anyway?
Natural language understanding (NLU) has always been the painful part while building a chatbot. How do you make sure your bot is actually understanding what the user says, and parsing their requests correctly? Well, here’s where DialogFlow comes in and fills the gap. It actually replaces the NLU parsing bit so that you can focus on other areas like your business logic!
DialogFlow is simply a tool that allows you to make bots (or assistants or agents) that understand human conversation, string together a meaningful API call with appropriate parameters after parsing the conversation and respond with an adequate reply. You can then deploy this bot to any platform of your choosing - Facebook Messenger, Slack, Google Assistant, Twitter, Skype, etc. Or on your own app or website as well!
The building blocks of DialogFlow
Agent: DialogFlow allows you to make NLU modules, called agents (basically the face of your bot). This agent connects to your backend and provides it with business logic.
Intent: An agent is made up of intents. Intents are simply actions that a user can perform on your agent. It maps what a user says to what action should be taken. They’re entry points into a conversation.
In short, a user may request the same thing in many ways, re-structuring their sentences. But in the end, they should all resolve to a single intent.
Examples of intents can be:
“What’s the weather like in Mumbai today?” or “What is the recipe for an omelet?”
You can create as many intents as your business logic desires, and even co-relate them, using contexts. An intent decides what API to call, with what parameters, and how to respond back, to a user’s request.
Entity: An agent wouldn’t know what values to extract from a given user’s input. This is where entities come into play. Any information in a sentence, critical to your business logic, will be an entity. This includes stuff like dates, distance, currency, etc. There are system entities, provided by DialogFlow for simple things like numbers and dates. And then there are developer defined entities. For example, “category”, for a bot about Pokemon! We’ll dive into how to make a custom developer entity further in the post.
Context: Final concept before we can get started with coding is “Context”. This is what makes the bot truly conversational. A context-aware bot can remember things, and hold a conversation like humans do. Consider the following conversation:
“Hey, are you coming for piano practice tonight?”
“Sorry, I’ve got dinner plans.”
“Okay, what about tomorrow night then?”
Did you notice what just happened? The first question is straightforward to parse: The time is “tonight”, and the event, “piano practice”.
However, the second question, “Okay, what about tomorrow night then?” doesn’t specify anything about the actual event. It’s implied that we’re talking about “piano practice”. This sort of understanding comes naturally to us humans, but bots have to be explicitly programmed so that they understand the context across these sentences.
Making a Reddit Chatbot using DialogFlow
Now that we’re well equipped with the basics, let’s get started! We’re going to make a Reddit bot that tells a joke or an interesting fact from the day’s top threads on specific subreddits. We’ll also sprinkle in some context awareness so that the bot doesn’t feel “rigid”.
NOTE: You would need a billing-enabled account on Google Cloud Platform(GCP) if you want to follow along with this tutorial. It’s free and just needs your credit card details to set up.
Creating an Agent
- Log in to the DialogFlow dashboard using your Google account. Here's the link for the lazy.
- Click on “Create Agent”
- Enter the details as below, and hit “Create”. You can select any other Google project if it has billing enabled on it as well.
Setting up a “Welcome” Intent
As soon as you create the agent, you see this intents page:
The “Default Fallback” Intent exists in case the user says something unexpected and is outside the scope of your intents. We won’t worry too much about that right now. Go ahead and click on the “Default Welcome Intent”. We can notice a lot of options that we can tweak.
Let’s start with a triggering phrase. Notice the “User Says” section? We want our bot to activate as soon as we say something along the lines of:
Let’s fill that in. After that, scroll down to the “Responses” tab. You can see some generic welcome messages are provided. Get rid of them, and put in something more personalized to our bot, as follows:
Now, this does a couple of things. Firstly, it lets the user know that they’re using our bot. It also guides the user to the next point in the conversation. Here, it is an “or” question.
Hit "Save" and let’s move on.
Creating a Custom Entity
Before we start playing around with Intents, I want to set up a Custom Entity real quick. If you remember, Entities are what we extract from user’s input to process further. I’m going to call our Entity “content”. As the user request will be a content - either a joke or a fact. Let’s go ahead and create that. Click on the “Entities” tab on left-sidebar and click “Create Entity”.
Fill in the following details:
As you can see, we have 2 values possible for our content: “joke” and “fact”. We also have entered synonyms for each of them, so that if the user says something like “I want to hear something funny”, we know he wants a “joke” content. Click “Save” and let’s proceed to the next section!
Attaching our new Entity to the Intent
Create a new Intent called “say-content”. Add a phrase “Let’s hear a joke” in the “User Says” section, like so:
Right off the bat, we notice a couple of interesting things. Dialogflow parsed this input and associated the entity content to it, with the correct value (here, “joke”). Let’s add a few more inputs:
PS: Make sure all the highlighted words are in the same color and have associated the same entity. Dialogflow’s NLU isn’t perfect and sometimes assigns different Entities. If that’s the case, just remove it, double-click the word and assign the correct Entity yourself!
Let’s add a placeholder text response to see it work. To do that, scroll to the bottom section “Response”, and fill it like so:
The “$content” is a variable having a value extracted from user’s response that we saw above.
Let’s see this in action. On the right side of every page on Dialogflow’s platform, you see a “Try It Now” box. Use that to test your work at any point in time. I’m going to go ahead and type in “Tell a fact” in the box. Notice that the “Tell a fact” phrase wasn’t present in the samples that we gave earlier. Dialogflow keeps training using it’s NLU modules and can extract data from similarly structured sentences:
A Webhook to process requests
To keep things simple I’m gonna write a JS app that fulfills the request by querying the Reddit’s website and returning the appropriate content. Luckily for us, Reddit doesn’t need authentication to read in JSON format. Here’s the code:
Now, before going ahead, follow the steps 1-5 mentioned here religiously.
NOTE: For step 1, select the same Google Project that you created/used, when creating the agent.
Now, to deploy our function using gcloud:
To find the BUCKET_NAME, go to your Google project’s console and click on Cloud Storage under the Resources section.
After you run the command, make note of the httpsTrigger URL mentioned. On the Dialoglow platform, find the “Fulfilment” tab on the sidebar. We need to enable webhooks and paste in the URL, like this:
Hit “Done” on the bottom of the page, and now the final step. Visit the “say_content” Intent page and perform a couple of steps.
1. Make the “content” parameter mandatory. This will make the bot ask explicitly for the parameter to the user if it’s not clear:
2. Notice a new section has been added to the bottom of the screen called “Fulfilment”. Enable the “Use webhook” checkbox:
Click “Save” and that’s it! Time to test this Intent out!
Reddit’s crappy humor aside, this looks neat. Our replies always drive the conversation to places (Intents) that we want it to.
Adding Context to our Bot
Even though this works perfectly fine, there’s one more thing I’d like to add quickly. We want the user to be able to say, “More” or “Give me another one” and the bot to be able to understand what this means. This is done by emitting and absorbing contexts between intents.
First, to emit the context, scroll up on the “say-content” Intent’s page and find the “Contexts” section. We want to output the “context”. Let’s say for a count of 5. The count makes sure the bot remembers what the “content” is in the current conversation for up to 5 back and forths.
Now, we want to create a new content that can absorb this type of context and make sense of phrases like “More please”:
Finally, since we want it to work the same way, we’ll make the Action and Fulfilment sections look the same way as the “say-content” Intent does:
And that’s it! Your bot is ready.
Dialogflow provides integrations with probably every messaging service in the Silicon Valley, and more. But we’ll use the Web Demo. Go to “Integrations” tab from the sidebar and enable “Web Demo” settings. Your bot should work like this:
And that’s it! Your bot is ready to face a real person! Now, you can easily keep adding more subreddits, like news, sports, bodypainting, dankmemes or whatever your hobbies in life are! Or make it understand a few more parameters. For example, “A joke about Donald Trump”.
Consider that your homework. You can also add a “Bye” intent, and make the bot stop. Our bot currently isn’t so great with goodbyes, sort of like real people.
Debugging and Tips
If you’re facing issues with no replies from the Reddit script, go to your Google Project and check the Errors and Reportings tab to make sure everything’s fine under the hood. If outbound requests are throwing an error, you probably don’t have billing enabled.
Also, one caveat I found is that the entities can take up any value from the synonyms that you’ve provided. This means you HAVE to hardcode them in your business app as well. Which sucks right now, but maybe DialogFlow will provide a cleaner solution in the near future!