Category Archives: Bot

Inaugural LDNBotFramework Meetup Retrospective

We recently wrapped up the first #LDNBotFramework meetup! There were a lot of lessons learned for me, as a first time meetup organiser, and overall I think it was a success.

The venue was great; the big video wall in JustEat’s Fleet Place House office combined with a mic and speaker system that “just works”, a stocked beer fridge and far too much pizza, all made for a perfect tech meetup setup.

LDNBotFramework Team #1!

Erdeniz Hassan, Simon Michael, Robin Osborne, and David Low

Thanks to @beanbaglabs for this group pic!

Sessions

We were very lucky to have representation from Microsoft to kick things off, then some great insights from SkyScanner’s case study, finishing off with a lightning talk on user expectations from JustEat.

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Implementing LUIS Routing within BotFramework

In the previous LUIS article, I introduced how to set up and train (and publish) a LUIS language interpreting web service, getting an “intent” and extracting “entities” from a given “utterance”

In this article I’ll use LUIS to enhance your botframework chatbot

If you haven’t done so already, create your bot using botframework, and set up a LUIS application.

Now that we’ve laid the foundations, let’s build a house. A ..um. chatbot house.. yeah.

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The Chatbot Revolution and The London BotFramework Meetup Group

If you’re one of the few people who have managed to avoid the onslaught of Chat Bot related articles over the past year, then let me start by way of an introduction; a chatbot is, in it’s most basic form, a computer program that can mimic basic human conversations.

This isn’t particularly new or exciting; this sort of chat bot has been around since the 70s. What is new and exciting is the recent development in systems and frameworks which make creating your own chat bot easy enough that you can focus on the quality of the interaction with the end user instead of wallowing in the technical considerations.

There is a website with a form to fill in that will give you a chat bot at the end of it, all the way through to an enterprise company’s framework for building your bespoke conversational interface from scratch.

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LUIS Natural Language Service for BotFramework

Creating a hosted bot using Microsoft’s botframework couldn’t be easier; hopefully you’ve had a chance to create one already, and if not there’s a great introduction to creating your first bot right here.

In the previous article we saw how to create a QnA (aka FAQ/Knowledge Base) service using a little known QnA Maker service of the botframework.

In this post we’ll start to create a more intelligent bot; one which can appear to understand the intent of the incoming message and extract specific key variables from it.

Understanding the intent of a piece of text is a really tricky problem to solve; totally out of scope for this article, and for most bot projects! Luckily, the botframework has a friend called LUIS – the Language Understanding Intelligence Service.

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Botframework Data URI images

A quick botframework tip – you can include images in your message attachments by Data URI, not just by URL!

For example, constructing a message like this:

var reply = message.CreateReply("Here's a **datauri image attachment**");
reply.Attachments = new List<Attachment> {
    new Attachment()
    {
        ContentUrl = "data:image/jpeg;base64,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",
        ContentType = "image/jpg",
        Name = "datauri"
    }
};

Gives a response that looks like this:

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Create your first QnA bot using botframework’s QnA Maker

When talking about the botframework, and chatbots in general, people usually assume that these are all using some clever logic and Natural Language Processing (NLP) to deliver a chunk of business logic via a natural language interface.

With the botframework this is most likely implemented by wiring up the Language Understanding Intelligent Service (LUIS): originally a stand-alone, (optionally) self-training, natural language understanding service, but now part of Microsoft Research’s Cognitive Services – previously “Project Oxford” – a collection of extremely powerful machine learning APIs for processing images, video, text, speech, to extract meaning.

Exceptionally powerful, incredibly clever stuff.

Almost all botframework articles and tutorials you’ll see at the moment will either do very basic pattern matching to extract intent from a message, or they’ll use LUIS (or a combination of the two); how to use LUIS is the subject of another article entirely, since this is no small task (I’ll come back to this in another article).

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Debugging BotFramework locally using ngrok

No doubt you’re already having lovely long conversations with your bot via Skype (or Facebook Messenger, or even SMS!) built using the botframework, and by using the Bot Emulator you can run your bot locally and debug it.

However, once it’s deployed and is being called via the Bot Connector framework, instead of directly, things get a bit tougher.

If you haven’t managed to override the – rather nasty – default exception handling that swallows exceptions and spews out reams of useless stack trace, then you may not have much idea what’s going on with your deployed bot, since all you get back is “Sorry, my bot code is having a problem.”

When you encounter a strange problem whilst conversing with your bot in Skype, going through the process of adding loads of logging and redeploying, trying again, checking logs – just to see the journey your bot code is going through – isn’t the most efficient.

If only you could debug the code on your development PC just as easily as you could before the bot was deployed, locally in Visual Studio…

In this article I’m going to show you how to debug your bot code from Skype though to your local PC’s Visual Studio instance, thanks to the amazing ngrok!

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Persisting data within a conversation with botframework’s dialogs

In the previous botframework article I covered the different types of responses available for the botframework. This article is going to touch on the Dialog and persisting information between subsequent messages.

So what’s a Dialog?

Dialogs can call child dialogs or send messages to a user. Dialogs are suspended when waiting for a message from the user to the bot. Dialogs are resumed when the bot receives a message from the user.

To create a Dialog, you must implement the IDialog<T> interface and make your dialog class serializable, something like this:

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Rich botframework conversation cards

In the last article about bots I covered creating a basic bot using Microsoft’s botframework, setting up Azure, deploying the bot into Azure, and configuring it to work within Skype.

In this article we’re going to investigate the various response types available to us in the botframework to develop a more rich conversational experience.

Markdown

Luckily you’re not limited to plain text in a bot conversation; we’re able to embed images, add attachments, give headers and subheaders, add a button or link, tap events for various areas, as well as use markdown to format the main text content.

If you’re not already familiar with Markdown, then get on the case! It means you can very easily write HTML by using a shorthand syntax which can easily be converted to HTML.

I’ve been using it for many years for blogging and general documentation; using pandoc you can even convert markdown to PDF or a Word Doc. Using remark.js or the more recent Marp you can use it to easily create PowerPoint-like presentations

markdown example

Botframework messages support using this syntax to make the responses more rich. Of course, for this to work, the attached service needs to know how to render the response (and I’ll get on to this later)

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