Our mission is to inspire and empower thousands of brands to connect with their customers in a meaningful way, giving millions of consumers a voice and improving their overall experiences. DiALOGiFY crafts, implements and automates the best interaction experience at any touchpoint. Get essential storytelling automation and ecommerce personalization for your business, without in-house development or project management - We do it for you. 1. Design your perfect strategy We work with you to create the best personalization and recommendation strategy. DiALOGiFY is not a generic, off-the-shelf software. Based on your goals and needs, we'll figure out how to use DiALOGiFY most effectively on your platform. 2. Easy integration and deployment DiALOGiFY integrates seamlessly with your existing tech system - including Shopify, Salesforce, Mailchimp, ActiveCampaig, Hubspot and more. DiALOGiFY is tailored to your platform, so you get value where you need it most. 3. Improve brand loyalty and shopping experiences everywhere You can introduce personalized customer experiences offline and online: on products, billboards, landing pages, category pages, product pages, banners, home pages, and more by simply adding a line of code or a qr-code. Use AI, recommendations, and personalization across your entire platform, quickly and easily. 4. Your dedicated success team We're here for you even after you launch. Our dedicated success team handles reporting and ongoing optimization to help you continually increase your CLV through better customer relationships, e-commerce personalization, and referrals. The new era of marketing is about building long-term relationships, no longer just transactional. We combine bot dialogs, marketing automation, storytelling, behavioral science and apply best practices from the gaming industry. It’s going to be a journey and we are your partner.

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There are numerous posts about developing a chatbot using Dialogflow. But creating chatbot isn’t enough. Connecting Dialogflow to the web interface is even more interesting and challenging. With Angular being a popular and emerging platform, here is our guide to integrate Dialogflow chatbot with Angular JS. In our quest to simplify chatbot integration with different platforms, we have previously written guides to integrate chatbots with websites and apps. Pre-requisites To get started, you would need a chatbot or working knowledge of Dialogflow and Angular JS. To integrate the chatbots with Angular, you will need a account. All the aforementioned tools have free to try out. Dialogflow Kommunicate Steps to integrate Dialogflow chatbot with Angular JS I am going to explain how I connected Dialogflow and Angular with the help of Kommunicate. Note: To keep it very simple and straight this tutorial explains with really basic and plain code development. Also to mention this project can be found on my Github . To get an in detail tutorial on how to integrate the bot into a website you can check here . Step 1: Create your Dialogflow chatbot To get started, you can easily create a chatbot in Dialogflow or edit one of their provided sample chatbot templates. To go further, you can create your own Intents & Entities. Step 2: Integrate Dialogflow chatbot with Kommunicate Login to your Kommunicate dashboard and navigate to the . If you do not have an account, you can create one . Locate the Dialogflow section and click on Settings. Bot section here Now, navigate to your Dialogflow console and download the service account key file. Here are the steps to locate the file: Select your Agent from the dropdown in the left panel.Click on setting button. It will open a setting page for the agent.Inside general tab search for and click on your service account.After getting redirected to your , create a key in JSON format for your project from the section and it will get automatically downloaded.Now upload the . GOOGLE PROJECTS SERVICE ACCOUNT actions key file If you are using V1 APIs, just copy your client and developer token from Dialogflow console and paste in the fields provided. Click on Save and Proceed. Setup your bot’s name and profile picture and choose whether to allow the for your newly created bot. Click and voila, your bot is now integrated. bot to human handoff Finish bot integration setup You can check/edit your integrated bot using the below path: Dashboard →Bot Integration → Manage Bots: You can check all your integrated bots here. Dashboard → Bot Integration. Once finished, your Dialogflow section will have a green icon with the number of bots are you have successfully integrated. Step3: Install the Angular CLI globally. To install the CLI using npm, open a terminal/command prompt and enter the following command: npm install -g @angular/cli Now, create a new workspace and initial app project. Run the CLI command ng new and provide the name my-app, as shown here: ng my-app new The initial app project contains a simple Welcome app, which is ready to run. Angular includes a server so that you can easily build and serve your app locally. Now, navigate to the workspace folder (my-app). Launch the server by using the CLI command ng serve, with the –open option. cd my-app
ng serve --open Step 4: Installing chatbot in the Angular component Install chatbot on the angular component. The CLI will create the first Angular component. It is the root component and is named app-root. Run any code editor and open workspace folder (my-app). Note: Components are the fundamental building blocks of Angular applications. They display data on the screen, listen for user input, and take action based on that input. @Component({ : , : , : [ ]
}) {
  title = ;
} selector 'app-root' templateUrl './app.component.html' styleUrls './app.component.css' export class AppComponent 'My First Angular App!' To install the chatbot, open Kommunicate and navigate to Dashboard →Settings. Click on under the Configuration section. Copy the JavaScript code to be added either in your website or your application. Install Paste the javascript code into the app.component.ts file. The code of the component should look like this @Component({ : , : , : [ ]
}) {
  title = ;
  ngOnInit() {
    ( { kommunicateSettings = { : <CONVERSATION_TITLE>};
      var s = document.createElement("script"); s.type = "text/javascript"; s.async = true;
      s.src = "https://api.kommunicate.io/v2/kommunicate.app";
      var h = document.getElementsByTagName("head")[0]; h.appendChild(s);
      (window as any).kommunicate = m; m._globals = kommunicateSettings;
    })(document, (window as any).kommunicate || {});
  }
} selector 'app-root' templateUrl './app.component.html' styleUrls './app.component.scss' export class AppComponent 'my first app' ( ) function d, m var "appId" ,"conversationTitle": < > YOUR_APP_ID The ng serve command launches the server, watches your files and rebuilds the app as you make changes to those files. The –open (or just -o) option automatically opens your browser to . http://localhost:4200/ If you run the browser, you should see a chat widget screen pop up and that means your chatbot is ready. This is how you integrate Dialogflow chatbot with Angular using Kommunicate in few simple steps. This article was originally published here

How to Integrate Dialogflow Chatbot with AngularJS [Step-By-Step Guide]

In this article, we will be sharing steps to integrate chatbot into your app. All you need to build a sample for an android app is and dialogflow Android chatbot Dialogflow Kommunicate. Below is an example of developed in android using Dialogflow. We actually use this bot on our website. If you wish to see the bot live in action, head . Kommunicate Support Bot here The actionable rich messaging powered bot can reply based on whether users are on chat for general queries, technical queries or scheduling a demo. You can use your existing dialogflow bot or checkout to build a qualifying bot of your own. Download the Kommunicate Support Bot from and import into your Dialogflow account. bot samples here Step 1: Setup an account in Kommunicate This is fairly simple. You can get a free account in . Signup and navigate to the . Click on in Dialogflow block. Kommunicate Bot section Settings Upload your Dialogflow provided client keys. In case you are using Dialogflow V1, you can copy paste your client and dev tokens. Though, we recommend using Dialogflow V2 for the latest capabilities. Step 2: Install Dialogflow integrated Android Chatbot SDK into your app Add Android SDK to your app Installing Kommunicate in your Android app is easy and fast. Kommunicate SDK comes with pre-configured dialogflow integration. Add the following in your app build.gradle dependency: Installation implementation 'io.kommunicate:kommunicate:1.6.3' **Initialize SDK**After the Gradle sync has finished with kommunicate dependency, you can initialize the SDK by calling the below method: Kommunicate.init(context, Your-APP-Key); You can get the Application Key by signing up on Kommunicate Dashboard.For full documentation, refer . here Sample Android app with Dialogflow Chatbot Here is a sample android app which has dialogflow chatbot integrated. and run it in android studio. Download Step 3: Launch Android chat app with Dialogflow integrated chatbot Now, you can send payload to dialogflow through chat screen and get text response from Dialogflow Agent.Kommunicate provides a ready to use Chat UI so only launching the chat screen is required in this step. Here are the instructions for launching the chat with Dialogflow bot: List<String> agentList = new ArrayList();agentList.add("agent1@yourdomain.com"); //add your agentID List<String> botList = new ArrayList();botList.add("bot1"); //enter your integrated bot Ids Kommunicate.launchSingleChat(context, "Support", Kommunicate.getVisitor(), false, true, agentList, botList, new KmCallback(){@Overridepublic void onSuccess(Object message) {Log.d(context, "ChatLaunch", "Success : " + message);} @Overridepublic void onFailure(Object error) {Log.d(context, "ChatLaunch", "Failure : " + error);}}); That’s all! Run the Android app and chat with the Dialogflow bot. You can easily integrate Dialogflow in Android apps in a few simple steps. In case you need more information, you can check out the Kommunicate documentation. Dialogflow Android SDK Dialogflow provides Android Client SDK that makes it easy to integrate speech recognition with API.AI natural language processing API on Android devices. If you are looking to develop your own chat implementation and directly integrate dialogflow into your android app without kommunicate SDK, then integrate with the following SDK. Github — Dialogflow Android Client SDK Read more about dialogflow and bot integration here: Integrate Dialogflow (Api.ai) Bot into Website Beginner’s Guide to Creating Chatbots Using Dialogflow This article is originally published . here

Build Android Chatbot with Dialogflow (API.ai)

There are many platforms with NLP, such as: chatbot Rasa: Open source conversational AI Dialogflow Bot Framework Wit.ai Amazon Lex — Build Conversation Bots We evaluated most noteworthy of bot platforms for building chatbots for customer support and service industry. Finally, and Rasa came on top in our priority list. Dialogflow This article covers the differences between Dialogflow and Rasa, and helps you understand which one to choose. Dialogflow Dialogflow is a Google-owned product. They have developed human-computer interaction technologies based on natural language conversations. Dialogflow is a complete closed source product with API and web interface. Dialogflow was previously known as api.ai and got by Google in 2016. acquired Rasa Rasa is an open source machine learning tool for developers and product teams to expand bots beyond answering simple questions.Rasa comes with Rasa NLU and Rasa Core. The NLU handles intents and entities while the Core handles dialogues and fulfillment. Dialogflow vs Rasa — Major Differences First of all, we found Dialogflow to be super easy to use. Almost everything is available in the user interface and you can create a bot without writing even a single line of code. Dialogflow provides everything from Entities, Intent, Training, Analytics, History to Integrations. They are available in the user interface. It is like a full-fledged ready to use service. In contrast, you will have to build things up on top of Rasa. Also, it requires python development for customizing the bot. Projects providing UI in Rasa Articulate Rasa UI Dialogflow meet everyone’s demand and it is more generalized. In Rasa, you can customize the bot and it requires python development. Free and paid plans are available in both. How to migrate from Dialogflow to Rasa? Migrating from Dialogflow to Rasa is easy. Dialogflow provides a way to export the agent in zip files. You can then train Rasa NLU using the same downloaded data. Then modify the API URL to point to Rasa NLU server. Detailed instructions are available . here Conclusion We wanted more control over customization so went ahead with Rasa for building our custom bots such as . For basic bots and customers who are already using Dialogflow, we added support for easy . Liz dialogflow integration How do you rate Dialogflow vs Rasa and why? Let us know in the comments below, we would love to hear them. This article was originally posted here.

Dialogflow vs Rasa — Which One to Choose?

On April 17, 2018 version 2 of the API . It marked the end of a beta phase that started back in November 2017. Dialogflow was made generally available Our API V2 now serves as the default API for all new Dialogflow agents, and all new feature upgrades will only be released for API V2. I remember reading the above in the announcement blog post, telling myself to upgrade my “ ” Google Assistant bot as a way to learn about the new API. Last weekend, I did exactly that and this blog post explains my approach. I Owe You Target Audience The main focus of this post is on the migration from an existing Dialogflow project that uses v1 of the API to the new API v2. If you’re new to Dialogflow, I recommend to get started with it contains the necessary steps to create a Dialogflow project. You won’t need to read the rest of this article since v2 is now the default API version. https://dialogflow.com/docs/getting-started/basics, API V1 I originally started with an “ ” project, which means my project directory structure looked like this: Actions on Google .firebasercfunctions├── index.js└── package.json The file contained the fulfillment logic for my bot, similar to the following: index.js // index.jsconst App = require("actions-on-google").DialogflowApp; const welcome = app => {app.ask('Welcome! How can I help you today?');} exports.iOweYou = functions.https.onRequest((request, response) => {const app = new App({ request, response }); // Create the action maplet actionMap = new Map();actionMap.set('input.welcome', welcome);// ... custom actions added here app.handleRequest(actionMap);}); As a visualization, the API v1 setup looked like this: Migration Guide If you’re in a similar situation as me and want to upgrade to v2, I highly recommend reading the official migration guide at . https://dialogflow.com/docs/reference/v1-v2-migration-guide My bot doesn’t have an integration to Slack, Skype, or any of the other integrations Dialogflow supports. All I had to migrate was the fulfillment endpoint. There is specifically for this use case and it lists the necessary steps. If your bot has an integration enabled, please refer to the official migration guide for details. a chapter in the migration guide In summary, migrating a fulfillment endpoint requires you to create a new agent, import the v1 agent (so you don’t have to create intents, entities, etc. from scratch), enable v2 on the new agent, upgrade your fulfillment code, test it all, release to production. In the v2 test agent, the fulfillment webhook URL needs to point to a new endpoint, the one we are going to migrate to v2. Open Dialogflow, select your v2 test agent and click on “Fulfillment” in the navigation. In the webhook’s URL field, append to the already populated URL. V2 By now, we have a v2 agent we can use for testing that points to a v2 fulfillment endpoint. What’s missing? The actual v2 of the fulfillment endpoint. Let’s start with a diagram: The main file exports two functions. One is the original that serves production traffic and the other one is a version that is used by the new test agent we upgraded to the Dialogflow API v2. index.js iOweYou iOweYouV2 To keep things organized, I suggest you extract the v1 code into a separate file and create an file for the new code. index-v1.js index-v2.js Dialogflow Fulfillment Library Beta As part of that migration, I decided to take advantage of this new Node.js fulfillment library. You can read more about it and see examples at . https://github.com/dialogflow/dialogflow-fulfillment-nodejs While the “I Owe You” bot currently only supports Google Assistant, using the fulfillment library is a great starting point to enable integrations to 8 chat and voice platforms in total down the road. API V2 Once is ready and well tested, it’s time to migrate the production traffic to the new API. Given the separation of v1 and v2 fulfillment code in individual files, migrating the production traffic to v2 is simple. The following diagram outlines what’s required: index-v2.js The steps include: Make sure the exported property loads the index file. iOweYou -v2 In Dialogflow, open your production agent and switch to the API v2. I suggest you run it like that for a while, monitor the logs and if anything goes wrong, revert step 1 and 2 above to roll back to the v1 implementation. At this point, the bot in production runs on the Dialogflow API v2. Once you’re satisfied and see no errors or drop offs in your conversations, it’s safe to clean up the code and remove the file. At this point you can also remove your test agent in Dialogflow. index-v1.js Simple, isn’t it? If you have any questions or suggestions, please leave a comment and I’ll get back to you.

Migrating to the Dialogflow API v2

, and are the 3 main components of any DialogFlow agent Intents contexts entities DialogFlow was founded back in 2010 and got acquired by Google in 2016. The beauty of DialogFlow, is that we can use it to build an agent and then integrate it with voice or text across multiple services. Services like Amazon Alexa, Google Assistant, Facebook Messenger, Twitter, etc… Supported integrations 1. Intents An intent represents a mapping between what a user says and what action our app need to take. Usually we would have more than one intent in an agent. Think of this as a decision tree and we need to map a user's phrase to a specific outcome/response. There is also this concept of a , in case the agent did not know how to respond to something. fallback intent 2. Contexts Contexts guide the conversation because they tell us what we know at any given point. Let us imagine a conversation flow like: Search for jobs in Wellington Order by date them The second conversation item (intent), can have an input context (like search criteria). We can also ask the user to provide the missing input context to be able to correctly trigger the intent. 3. Entities They are the values in user says like location, date, etc… DialogFlow has some pre-defined entities like address, city, etc…, these are called system entities. There are also the entities we define in our agent, which are called developer entities. Finally, we would most probably also need a that goes with our agent. Which we could either implement and host it on our own or use Firebase Cloud Functions (just like Amazon Lambdas) for that. backend Final thoughts Back in the early days of personal computers, users had to interact with the console. A black screen with few to no options to choose from. Then came visual interfaces. It became more obvious what a piece of software “can do”. Now we are moving to a sort of ambiguous form of interacting with our machines again, using voice. It is ambiguous because we cannot just speak naturally with it and it can be tricky to figure out what it is capable of doing. yet If you enjoyed reading this story, please and share it to help others find it! You can also follow me to get notified when I publish a new story. 👏 on Twitter

The 3 main components of a DialogFlow agent

I have seen many posts on how to build a chatbot for a wide variety of collaboration platforms such as , , … So I decided to build a chatbot from scratch to production using latest release , and . Slack Facebook Messenger HipChat Angular v5.0.0, DialogFlow AWS Here is how our will look like at the end of this post: chatbot Note: This project is open source and can be found my . Github To get started, create a brand new Angular project using the : Angular CLI ng new smartbot — style=scss 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: ng generate class models/message The message entity has 3 fields: 3 — Message List Component Generate a new component: ng generate component components/message-list Now we can display the messages by iterating over them: The code of this component should look like this: Note the usage of instead of the relative path, its called . To be able to use aliases we have to add the properties to our file like this: @app/models alias paths tsconfig.json Note: I also added alias to be able to access environment variables from anywhere in our application. @env 4 — Message Item Component Let’s build a component that will simply display a message in our message list: ng generate component components/message-item In , add the following content: message-item.component.html 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: ng generate component components/message-item In the , add the following content: message-form.component.html And it’s corresponding typescript code in message-form.component.ts: The method will be called each time a user click on send button. sendMessage() That’s it! Try it by yourself and you will see that it’s working. ng serve At this moment, you wont get any response, that’s where comes to play. NLP 6 — NLP Backend I choose to go with Sign up to and create a new agent: DialogFlow. DialogFlow Then, enable the feature to have a simple chitchat: Small Talk Note: You can easily change the responses to the questions if you don’t like them. To go further you can create your own & as described in my . Intents Entities previous tutorial Copy the . It will be used for making queries. DialogFlow Client Access Token Past the token into your file: environments/environment.ts 7 — DialogFlow Service Generate a which will make calls the to retreive the corresponding response: DialogFlow Service DialogFlow API ng generate service services/dialogflow It uses the to process natural language in the form of text. Each API request, include the field in the . DialogFlow API Authorization HTTP header Update the method in as follows: sendMessage() MessageFormComponent Finally, in copy and past the following code to include the and the directives: app.component.html, message-list message-form 8 — Deployment to AWS Generate production grade artifacts: ng build — env=prod The build artifacts will be stored in the directory dist/ Next, create an with : S3 bucket AWS CLI aws s3 mb s3://smartbot-mlabouardy Upload the build artifacts to the bucket: aws s3 cp dist/ s3://smartbot-mlabouardy — recursive — grants read=uri=http://acs.amazonaws.com/groups/global/AllUsers Finally, turns website hosting on for your bucket: aws s3 website s3://smartbot-mlabouardy — index-document index.html If you point your browser to the , you should see the chatbox: S3 Bucket URL

Chatbot with Angular 5 & DialogFlow

(formerly ) gives users new ways to interact with your bot by building engaging voice and text-based conversational interfaces powered by . DialogFlow API.AI AI offers many in different programming languages: DialogFlow SDKs But unfortunately, there’s no for SDK Golang But dont be sad, I made an to integrate with : SDK DialogFlow Golang This library allows integrating agents from the natural language processing service with your application. DialogFlow Golang Issue the following command to install the library: go get github.com/mlabouardy/dialogflow-go-client 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 , on how to create a in to show list of movies playing in cinema, and tv shows airing on TV: previous tutorial Messenger Bot Golang

DialogFlow (API.AI) Golang SDK

This post is part of “ ” series. In this part, I will show you how to create a in with (formerly ) to show list of movies playing today in cinema. ChatOps Messenger Bot Golang DialogFlow API.AI Note: all the code used in this demo can be found on my . Github Start with an server exposing 2 endpoints: HTTP 1 — GET /webhook Handles challenge verification. It simply looks for the and responds with the sent in the verification request. Facebook Verify Token challenge 2 — POST /webhook Handles messages coming from : Messenger It calls the method which uses to send a image to the user: ProcessMessage Facebook Graph API GIF Note: for more in depth details check my tutorial Build a Facebook Messenger bot with Go and Messenger API Create a It will be “ ” of your bot: Facebook page. identity Then create a . It will be the middleware that connects the server and your public page. Facebook application Click from the left menu, then choose Add Product Messenger: At the , choose the page you just created from the dropdown menu, and it will generate a token: Token Generation Once you’ve gotten your and , make sure you add those two as environment variables for the server: PAGE_ACCESS_TOKEN VERIFY_TOKEN export PAGE_ACCESS_TOKEN=”YOUR PAGE ACCESS TOKEN”export VERIFY_TOKEN=”YOUR SECRET” In new terminal session, issue the following command to start the server: HTTP go run *.go In order to make our server publically accessible, I will use a tool called . It basically creates a secure tunnel on your local machine along with a public URL you can use for browsing your local server. ngrok Note: Keep in mind, to use your bot in production, you need to use a : like , IaaS AWS GCP, Azure … like PaaS Heroku, Clever Cloud … ngrok http 5000 Then, at the , click the button: Webhooks section Setup Webhooks After you’ve configured your , you will need to subscribe to the page you created earlier: Webhook Go to the you created and and click on “ ” button, next to the “ ” button near the top of the page. Start sending your Page messages and the bot should reply with a ! Facebook Page Message Like GIF By default, the bot should respond to everything with a image. GIF Now lets make it smarter, for that we will use an ( ) backend like (formerly ): NLP Natural Language Processing DialogFlow API.AI So after signing up to , create a new Agent: Dialogflow Give it a name and fill out the required fields: Once created, lets use feature of to give our bot the ability to have simple conversations: Small Talk DialogFlow Enable the 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: Small Talk To test it out, you can use the at the right hand side: Console Now lets use this feature in out bot. offers many in different programming languages: DialogFlow SDKs But unfortunately, there’s no for SDK Golang But dont be sad, I made an to integrate with : SDK DialogFlow Golang So, install library: DialogFlow Golang go get github.com/mlabouardy/dialogflow-go-client Go back to dashboard and copy the : DialogFlow Client Access Token Set it as environment variable: export DIALOG_FLOW_TOKEN=”YOUR TOKEN” Create a new function that takes the message sent from a user via as an argument, and pass it to to get the appropriate response: Messenger DialogFlow Client Go to the and click on to start chatting : Facebook Page Message 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 , to store the type of the show ( or ) the user is asking about: entity movie series Then, create a new , which represents a mapping between what a user says and what action should be taken: intent Create some more questions: Finally, update the method to respond with a list of shows if the intent name is . The method is using the library to get the list of shows. ProcessMessage shows moviedb Let’s test the bot from : Messenger Wow !! you have created your first in with ! It was easy, wasn’t it ? chatbot Golang DialogFlow In the upcoming tutorial, I will show you how to create a with & . Serverless Messenger Bot Lambda API Gateway

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