Amazon Alexa GitHub Followers Counter

This post is part of “Alexa” series. I will walk you through how to build an Amazon Alexa Skill with Node.JS and Lambda to get numbers of followers & repositories in GitHub in real-time.

Note: all the code is available in my GitHub.

Amazon Echo will captures voice commands and send them to the Alexa Skill to convert them into structured text commands. A recognized command is sent to an AWS Lambda function that will call GitHub API to get response.

To get started, sign up to Amazon Developer Console,  and create a new Alexa Skill:

The invocation name is what user will say to trigger the skill. In our case it will be “github“.

Click on “Next” to bring up the Interaction Model page, use the intent schema below:

Intents will map user’s voice command to services that our Alexa skill can address. For instance, here I defined an intent called GetGithubFollowerCount, which will line up with a portion of code in my Lambda funtion that I leverage in a bit.

The programming languages are defined as a Custom Slot Type, with the following possible values:

Now our intents are defined, we need to link them to a human request that will trigger this linkage. To do this multiple sentences (utterances) are listed to make the interaction as natural as possible.


Click on “Next” and you will move onto a page that allows us to use an ARN (Amazon Resource Name) to link to AWS Lambda.

Before that, let’s create our lambda function, login to AWS Management Console, then navigate to Lambda Dashboard and create a new function from scratch:

Select Alexa Skills Kit as trigger:

I wrote the Lambda functions in Node.JS, although that code isn’t actually that interesting so I won’t go into it in much detail.

This function is fired when there is an incoming request from Alexa. The function will:

  • Process the request
  • Call GitHub API
  • Send the response back to Alexa

Create a zip file consisting of the function above and any dependencies (node_modules). Then, specify the .zip file name as your deployment package at the time you create the Lambda function. Don’t forget to set your GitHub Username as an environment variable:

Back in the Alexa Skill we need to link our Lambda function as our endpoint for the Alexa Skill:

That’s it, let’s test it out using a Service Simulation by clicking on “Next“.

GetFollowerCount Intent : 

GetRepositoryCount Intent:

GetGithubRepositoryCountByLanguage Intent:

You can see that the Lambda responds as expected !

Test it now with Amazon Echo, by saying “Alexa, ask GitHub for …” :


Chatbot with Angular 5 & DialogFlow

I have seen many posts on how to build a chatbot for a wide variety of collaboration platforms such as Slack, Facebook Messenger, HipChat … So I decided to build a chatbot from scratch to production using Angular latest release v5.0.0, DialogFlow, and AWS.

Here is how our chatbot will look like at the end of this post:

Note: This project is open source and can be found on my Github.

To get started, create a brand new Angular project using the Angular CLI:

1 – Chatbot Architecture

We will split out chat app in different components and each component will be able to communicate with others using attribute directives:

2 – Message Entity

Create an empty class by issuing the following command:

The message entity has 3 fields:

3 – Message List Component

Generate a new component:

Now we can display the messages by iterating over them:

The code of this component should look like this:

Note the usage of @app/models instead of the relative path, its called alias. To be able to use aliases we have to add the paths properties to our tsconfig.json file like this:

Note: I also added @env alias to be able to access environment variables from anywhere in our application.

4 – Message Item Component

Let’s build a component that will simply display a message in our message list:

In message-item.component.html, add the following content:

The code of the component should look like this:

5 – Message Form Component

Let’s build the form that will be responsible for sending the messages:

In the message-form.component.html, add the following content:

And it’s corresponding typescript code in message-form.component.ts:

The sendMessage() method will be called each time a user click on send button.

That’s it! Try it by yourself and you will see that it’s working.

At this moment, you wont get any response, that’s where NLP comes to play.

6 – NLP Backend

I choose to go with DialogFlow.  Sign up to DialogFlow and create a new agent:

Then, enable the Small Talk feature to have a simple chitchat:

Note: You can easily change the responses to the questions if you don’t like them. To go further you can create your own Intents & Entities as described in my previous tutorial.

Copy the DialogFlow Client Access Token. It will be used for making queries.

Past the token into your environments/environment.ts file:

7 – DialogFlow Service

Generate a DialogFlow Service which will make calls the DialogFlow API to retreive the corresponding response:

It uses the DialogFlow API to process natural language in the form of text. Each API request, include the Authorization field in the HTTP header.

Update the sendMessage() method in MessageFormComponent as follows:

Finally, in app.component.html, copy and past the following code to include the message-list and the message-form directives:

8 – Deployment to AWS

Generate production grade artifacts:

The build artifacts will be stored in the dist/ directory

Next, create an S3 bucket with AWS CLI:

Upload the build artifacts to the bucket:

Finally, turns website hosting on for your bucket:

If you point your browser to the S3 Bucket URL, you should see the chatbox:

DialogFlow (API.AI) Golang SDK

DialogFlow (formerly API.AI) gives users new ways to interact with your bot by building engaging voice and text-based conversational interfaces powered by AI.

DialogFlow offers many SDKs in different programming languages:

But unfortunately, there’s no SDK for Golang

But dont be sad, I made an SDK to integrate DialogFlow with Golang:

Résultat de recherche d'images pour "thank you sir meme"

This library allows integrating agents from the DialogFlow natural language processing service with your Golang application.

Issue the following command to install the library:

The example below, display list of entities:

Note: for more details about the available methods, check the project Github repository.

For a real world example on how to use this library, check my previous tutorial, on how to create a Messenger Bot in Golang to show list of movies playing in cinema, and tv shows airing on TV:

Messenger Bot with DialogFlow & Golang

This post is part of “ChatOps” series. In this part, I will show you how to create a Messenger Bot in Golang with DialogFlow (formerly API.AI) to show list of movies playing today in cinema.

Note: all the code used in this demo can be found on my Github.

Start with an HTTP server exposing 2 endpoints:

1 – GET /webhook

Handles Facebook challenge verification. It simply looks for the Verify Token and responds with the challenge sent in the verification request.

2 – POST /webhook

Handles messages coming from Messenger:

It calls the ProcessMessage method which uses Facebook Graph API to send a GIF image to the user:

Note: for more in depth details check my tutorial Build a Facebook Messenger bot with Go and Messenger API

Create a Facebook page. It will be “identity ” of your bot:

Then create a Facebook application. It will be the middleware that connects the server and your public page.

Click Add Product from the left menu, then choose Messenger:

At the Token Generation, choose the page you just created from the dropdown menu, and it will generate a token:

Once you’ve gotten your PAGE_ACCESS_TOKEN and VERIFY_TOKEN, make sure you add those two as environment variables for the server:

In new terminal session, issue the following command to start the HTTP server:

In order to make our server publically accessible, I will use a tool called ngrok. It basically creates a secure tunnel on your local machine along with a public URL you can use for browsing your local server.

Note: Keep in mind, to use your bot in production, you need to use a :

  • IaaS like AWSGCP, Azure …
  • PaaS like Heroku, Clever Cloud …

Then, at the Webhooks section, click the Setup Webhooks button:

After you’ve configured your webhook, you will need to subscribe to the page you created earlier:

Go to the Facebook Page you created and and click on “Message” button, next to the “Like” button near the top of the page. Start sending your Page messages and the bot should reply with a GIF !

By default, the bot should respond to everything with a GIF image.

Now lets make it smarter, for that we will use an NLP (Natural Language Processing) backend like DialogFlow (formerly API.AI):

So after signing up to Dialogflow, create a new Agent:

Give it a name and fill out the required fields:

Once created, lets use Small Talk feature of DialogFlow to give our bot the ability to have simple conversations:

Enable the Small Talk checkbox. With this feature enabled we imported a lot of predefined answers for simple questions and phrases. You can easily change the responses to the questions if you don’t like them:

To test it out, you can use the Console at the right hand side:

Now lets use this feature in out bot. DialogFlow offers many SDKs in different programming languages:

But unfortunately, there’s no SDK for Golang

But dont be sad, I made an SDK to integrate DialogFlow with Golang:

Résultat de recherche d'images pour "thank you sir meme"

So, install DialogFlow Golang library:

Go back to DialogFlow dashboard and copy the Client Access Token:

Set it as environment variable:

Create a new function that takes the message sent from a user via Messenger as an argument, and pass it to DialogFlow Client to get the appropriate response:

Go to the Facebook Page and click on Message to start chatting :

But that’s not enough, lets take this further and make our bot tell us about the movies playing today in cinema, and series airing today on TV.

Create an entity, to store the type of the show (movie or series) the user is asking about:

Then, create a new intent, which represents a mapping between what a user says and what action should be taken:

Create some more questions:

Finally, update the ProcessMessage method to respond with a list of shows if the intent name is shows. The method is using the moviedb library to get the list of shows.

Let’s test the bot from Messenger:

Wow !! you have created your first chatbot in Golang with DialogFlow ! It was easy, wasn’t it ?

Image associée

In the upcoming tutorial, I will show you how to create a Serverless Messenger Bot with Lambda & API Gateway.

Build a Facebook Messenger bot with Go and Messenger API

In this first tutorial of the “ChatOps” series , I will show you quickly how to create a Facebook Messenger Bot  in Golang. All the code used in this demo can be found on my Github

1 – Messenger bot

We will start by creating a dummy web server with one endpoint to print a hello world message ✌. I’ll use “gorilla/mux” package to do that. I found it much easier to setup routes for our server rather than using the go standard library.

We first need to install “gorilla/mux” library:

Then, create a file called app.go with the following content:

So basically, we have a main function which create a new route “/”, and associate to this path a HomeEndpoint function that use ResponseWrite reference to print a message. Then it start a HTTP server on port 8080.

To verify that things are working, start your local server with:

Then in your browser, visit http://localhost:8080 and you should see:


Now that you’ve got your first endpoint working, we will need to expose 2 endpoints to make it work with Facebook platform:

1.1 – Verification Endpoint: (GET /webhook)

It simply looks for the Verify Token and responds with the challenge sent in the verification request.

1.2 – Messages Handler Endpoint: (POST /webhook)

It serialize the request body into Callback object , then it parse it and fetch the message object and pass it as an argument to ProcessMessage function that will use Facebook Graph API to send the response to the user (in this case we will send an image):

Our local server url (http://localhost:8080) is not available to all the other people in the internet  and doesn’t support HTTPS which is necessary for Facebook Messenger bot. Therefore, we need to expose it to the public.

2 – Deployment

Note: You could also use a tool like ngrok. It basically creates a secure tunnel on your local machine along with a public URL you can use for browsing your local server. Keep in mind, to use your bot in production, you need to use a real IaaS like AWS, Heroku, Clever Cloud …etc

In this tutorial I will choose CleverCloud as IaaS provider, it deploys your Go application for free and offer you extra add ons like monitoring, logs, scaling, continuous delivery …

In order to deploy to CleverCloud you’ll need a CleverCloud user account. Signup is free and instant. After signing up:


We click on “Add an application”, then you can either upload your app code from local repository or from Github:

Next, We choose GO as our server side language. Then, we click on “Next

We left all fields as default, and we click on “Create

Our server does not use any external resources (MySQL, Redis …) so we will simply skip this part by clicking on “I don’t need any add-on

Congratulations !   You have successfully deployed your server.


Note: The app URL is :

ID is the string on the bottom right of the dashboard,

3 – Facebook Setup

3.1 – Create Facebook Page

If you don’t already have one, you need to create a facebook page that we will use to connect our bot to.

Just give it a name, and that’s it now you have created a Facebook page for you Bot 

3.2 – Create Facebook App

Once the page was created, we will create a facebook app which will be connected your webhook server and your public page:  which works as middleware that connects your Webhook (APP URL) and your public page.

You need to give your app a name then click on “Skip and Create App ID

After creating an app, we need to add Messenger as a platform. So we click on “Add Product” then select Messenger as a product

Now you’re in the Messenger settings. There are few things in here you’ll need to fill out in order to get your chatbot  wired up to the server endpoint we set up earlier.

3.2.1 – Generate a Page Access Token

Using the page we created earlier, you’ll get a random “Page Access Token

You need to copy the token to your clipboard. We’ll need it as an environment variable (PAGE_ACCESS_TOKEN) for our server.

3.2.2  – Setup subscription

Next, we click on “Setup webhook” button on “Webhooks” section, a new popup will show up:

  • Callback URL : Clever Cloud we set up earlier
  • Verify Token: A secret token that will be sent to your bot, in order to verify the request is coming from Facebook, Make sure to remember the value because we will need it as a second environment variable (VERIFY_TOKEN) for the server
  • Subscription Fields: represents which events you want Facebook to notify your webhook about, in this case, we will choose “messages

After you’ve configure your subscription, you’ll need to subscribe to the specific page, you want to receive message notification for

3.2.3 – Set environment variables

Once you’ve gotten your PAGE_ACCESS_TOKEN and VERIFY_TOKEN, make sure you add those two as environment variables for the server on clever cloud dashboard

Then restart the application and you should be good to go ! 

4 – Test the Bot

Go to your Facebook Page and send a message to it. You should see a gif back to you 

5 – Customize your Bot’s behavior

In this quick tutorial I showed you how to build a simple & dumb bot for facebook messenger, to make it smarter  and have more interactions with the user. We need to use a NLP backend like (Google), (Facebook) or And that will be the subject of my upcoming tutorial ❤