With an array of chatbots springing up in the last few years, major tech companies are deploying chatbots to acknowledge consumer/user concerns ranging from helping them choose the right product or service, to resolve issues faced by users. Most of the service-oriented companies have followed suit and resorted to address the growing user concerns. It may not be surprising that, in the coming days bots can completely replace humans to deliver a better user experience. In this article, we take a look at a .NET Chatbot application used and powered by AWS.
.NET and AWS
The .NET (pronounced DotNet) Framework is a software framework originally developed by Microsoft, that mainly contributed to its Windows operating system(OS) environment. The .NET Framework caters to a vast number of .NET platforms across, right from smartphones to cross-platform OS.
The application areas include content management systems in large tech companies and similar web applications. If you use any .NET application, you might encounter terms such as Framework Class Library (FCL) and Common Language Runtime (CLR). The former is a vast class library which helps interpret and interchange code written in other languages such as C++, Visual Basic, C# among others and the latter is a software environment that provides features such as memory management and exception handling. If .NET is strictly used for web services, then another software called Visual Studio goes hand-in-hand with .NET. Visual Studio, as the name suggests, gives a visual representation of the web service required for building an user interface for applications– for example, websites and even chatbots, among many other features.
How to use visual studio for c on mac. The Visual Studio Docker template project comes with already created Dockerfile (docker configuration blueprint) and this project is detected by the AWS Toolkit as a container project. From this point we can easily publish our project using the AWS Toolkit. The AWS Toolkit for Visual Studio 2017 is available via the Visual Studio Marketplace. The AWS Toolkit for 2013 and 2015 is contained in the AWS SDK and Tools for.NET install package. At this time, the AWS Toolkit for Visual Studio does not support Visual Studio for Mac.
Started in 2006, Amazon Web Service(AWS) is Amazon’s cloud computing arm. It provides services such as storage servers, bandwidth and custom support for Application Programming Interfaces (APIs). AWS is branched into many services with Amazon Elastic Compute Cloud (EC2), Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon CloudFront and Amazon Cognito being the popular picks in commercial usage for cloud.
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Prior Requirements To Start With The Bot Project
The AWS Product Portfolio for .NET
Before AWS is incorporated in a chatbot based on .NET, the domain relevant AWS services are to be known beforehand with the implementation. It has to be followed in the order mentioned as below:
AWS SDK for .NET provides .NET-specific APIs to work with AWS.
AWS in a .NET Chatbot
The AWS services mentioned earlier, will be integrated with a .NET chatbot which is developed prior to using AWS. The code is written for Linux and Windows using ASP.NET Model-View-Controller(MVC) Framework. (The .NET Chatbot application used here is for Windows OS only).
Step 1 – Building an Amazon Lex Bot
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The AWS web application first is started by specifying the region supported by AWS. Once this is done, a Lex Bot is created by clicking on ‘Create’ button in the Amazon Lex under Machine Learning on the AWS console. You can create a custom bot or select from the sample present in the screen shown below. Remember, Lex is working now on the .NET chatbot. After this, the bot is tested using the Test Chatbot button and is created by clicking on the Publish button.
Step 2 – Using Amazon Lambda for Lex
The Lambda function is now created and integrated with the Lex Bot to perform initialisation and validation. Lambda uses a concept called “Code Hook”. This means a part of code usually segregated as a module, can be altered to respond differently. This is done mainly to ensure whether the queries fall under the appropriate pattern for the bot.
Lambda function is created using four different options : (1) AWS Toolkit for Visual Studio(for Windows), (2) .NET Core Command Line Interface (3) AWS Management Console (4) AWS Command Line Interface. Once Lambda is created, it is subjected to testing. The testing is run in a ‘Configure Test Event’ module which opens after clicking on the Test button. After this, the Lambda is configured using the following path in the interface : AWS Management Console > Services > Machine Learning > Amazon Lex > Lambda Initialization and Validation. In this section, check-mark the ‘Initialization and Validation Code Hook’ which will present a drop-down menu to choose the Lambda function created. Also, under the Fulfillment menu, the AWS Lambda function is specified. The working of the code is validated for the bot.
Step 3 – Using Amazon Cognito to setup AWS credentials.
Now, in order to authenticate the bot to work for a set of users, a ‘Federated Identity Pool’ is created using Amazon Cognito. This is setup using the following path in the options present in the screen : AWS Console > Services > Mobile Services > Cognito > Manage Federated Identities > Create New Identity Pool. Once identity pool is created, the necessary parameters such as the provision for enabling access and other parametres. Check mark the options on the menu box which provides these options. A Pool ID is created which displays the sample code. Retain this Pool ID for making changes to access to the bot in the future.
Step 4 – Cloning the repository on GitHub and running the project.
The code is generally a data repository and is cloned so that there is a copy retained by the user along with uploading it to the server. For this project, it is cloned on GitHub. Then, to provide an Integrated Development Environment(IDE) to facilitate software development in the project, AWS Toolkit for Visual Studio is installed to configure on a Windows system with Visual Studio program installed. After this, the settings can be changed under the appsettings.json file for any changes presented in Step 1, 2 and 3. Also, .NET Core 2.0 can also be installed for testing the chatbot. The user can choose Visual Studio or .NET Core 2.0 Command Line for testing.
Step 5 – Deploying the Bot
The bot is now ready to be deployed online. This is done by using AWS CloudFormation template to deploy on the Windows server. Similarly, for Linux/Mac OS, deployment is done using CodeStar and CodeCommit developer tools.
Conclusion :
The above process may seem intimidating at first for anyone new to building chatbots, but with regular practice and experimenting with the process and code, the working aspects will be a piece of cake. In addition, AWS services are not free, so the user can try working the code on GitHub which is the largest software development platform where users can tinker with codes and software.
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June 28, 2018
Do you need a cross platform performant app that can run in the cloud, be performant, can easily scale and can be started quickly?
Nowadays, with .NET Core we have an open source cross platform optimized framework in which we can build apps quickly and with Docker we have the necessary operation system virtualization and a platform that is based on the microservice architecture built with mind to develop lightweight, secure, extensible application without worrying about the physical servers. Taking into consideration that with Docker we can run multiple building blocks (containers) independently and on every platform (Windows, Linux, Mac) makes this even greater solution.
One of the major players in the market where we can put our solution is Amazon Web Services (AWS) and the actual service that is compatible for this kind of solution is the Elastic Container Service (ECS).
On the last Re:Invent (November 2017), Amazon Web Services pre-announced the new technology AWS Fargate that from this year is available – AWS Elastic Container Service (ECS).
AWS Elastic Container service is a scalable, performant cloud computing container orchestration service that supports Docker and allows running containerized apps on AWS. With the new Fargate technology we can run containers without managing servers or clusters.
So, how can we start? The simplest way to start is by using the Visual Studio Docker template and Visual Studio AWS Toolkit.
The Visual Studio Docker template project comes with already created Dockerfile (docker configuration blueprint) and this project is detected by the AWS Toolkit as a container project. From this point we can easily publish our project using the AWS Toolkit.
When it comes to the docker image details there is a very easy way to specify the image name, docker repository and tag in a user friendly environment of the AWS toolkit which in the background executes docker native cli commands.
For the ECS launch specific configuration there is an additional configuration section where we sepcify the cluster name, launch type, compute capacity configuration and network configuration.
Also using the toolkit there is an easy configuration for the container services, number of tasks and application load balancing.
After just a few minutes we have functional app up and running with the public DNS endpoint. Hooray!
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Visual Studio Aws Sdk
Conclusion
Using .NET core in docker containers is the right choice because it gives us performance, isolation, extensibility and easily configurable environment to work with. With AWS Elastic Container Service (ECS) and Fargate technology we don’t have to worry about where our application will run and how to configure it. We can focus and concentrate on developing our app from day 0 which gives us great advantage especially when we are using .NET Core with Visual Studio as an IDE.
Visual Studio For Mac DownloadAuthorPetar GjeorgievComments are closed.
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