Category: Chat

Create chatbot using bot framework sdk and LUIS — Part 4

In the previous 3 parts we created a simple e-comm chatbot, we completed the create order conversation flow. Saved the order details in a csv. In this part we will be adding intelligence to our bot using LUIS.

We will complete following steps in this part:

• Create a LUIS app. Train and publish the app.
• Use the app in our bot service.
• Update create order dialog to handle slot filling. that is detecting missing entities in the order details and ask user to provide the values for the required entities.
• Complete create order flow

Microsoft Azure LUIS:

Microsoft’s Azure LUIS is NLU service which is available on Azure. For using LUIS we create LUIS app. Add intents and entities in our app. We also provide sample user utterances for the intents.

For a given text, LUIS app first detects to which intent the given text belongs to. In simple word it takes the users requirement in user’s own words and then interprets it to understand what exactly user’s need is. It then extracts the important words from the user’s text, which we call entities. For exapmple — in our user case, user provides the order description in his own words. In the order descrption we need to find the word which tells the item name like cake or pastry etc. similarly word whcih tells the flavour of the item like chocolate, cheese etc. So in this example item name and flavour are the entities.

For more details check below links:

• Intents and entities – LUIS – Azure Cognitive Services
• Entity types – LUIS – Azure Cognitive Services

Why we need LUIS in our bot service?

We are asking user to provide the order description in his own words. The bot needs to understand whether tthe order consists of only one item or more than one item. If more than one item are there in the order that means we need to refer each order item details as sub order and create multiple line items which an order. Also for ech item bot has to check whether the order detail is com plete or not for example if cake is requested then is the size and flavour of the cake are provided by the user or not. For identifying sub-orders in the order description and the required details i.e. entities in the order description we need th Natural Language understanding service. LUIS is the Natural Language Service which we will be using in our bot.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Create a LUIS app using the JSON provided:

You need to create a LUIS app, for that you should have an azure account subscription. Follow the steps mentioned here to create LUIS App.

Intents and entities required for our bot is defined in the json here.

Copy this orderApp.json on your machine. we will be importing this json in the LUIS app to add the required intents and entities in the luis app.

To import the json in your luis app. Go to your luis app, and click on the down arrow next to New App button.

Now select Import as json and browse to ordersApp.json file. This creates the intents and entities in your app.

Order entity is a structured entity. Thre are sub-entities defined under the main order entity. Explore each entity to understand them clearly. Check utternces defined for create_order intent. See how order entity and other sub-entities are marked in the utterances. These are the training data. You can add more training data to improve accuracy. Click on the test tap to test the app.

Above text returns below result, click on the inspect to see the detailed result. LUIS service has detected there are 2 orders in the given text. For each order it has identified the entities i.e. item_name, item_flavour etc. correctly. Expand item_size entity to check the values retrieved for size_unit and size_value entities. Our LUIS app is working prefectly!

Now we can publish the app. Click on the Publish button to publish the app. You will get 2 options Staging Slot and Production Slot. Select production slot. It will take few minutes to publish the app.

After the app is published, go to Manage tab. On Manage screen you will find “Azure Resources” option pane. Click on the “Azure Resources” option you will be on “Prediction Resources” tab. Copy the URL given in the Example Query. Add this URL in the config file or your project. Add a new attribute in config.py. Name the attribute “LUIS_APP_URL” set the value as the URL cpied from Azure Resources. Trim the text “YOUR_QUERY_HERE” from the URL (u will find this text at the end of the URL).

We will be using this URL to send the user provided order description text to the LUIS App.

Add LUIS App helper function:

Add a new script file luisApp.py and add the below code in it. Below code defnes a function that takes a text as input and pass it to the LUIS by concatenating the LUIS_APP_URL and the input text. The final URL is then invoked through get method. The reslt returned is a json which is then returned to the calling function.

Define class for sub-order details:

We will add a new class order_details.py in the project. This will have all order related attributes. Add below code in order_details.py

Add new attributes in user_details.py

In user_details we will add 2 new attributes orders_list and current_order. Orders_list is a list type attribute this will be used to maintain the list of sub-orders. current_order is int type attribute this is to keep track of the current sub-order while iterating through the sub-orders for chceking completeness of the su-orders.

Modify createorder_dialog to add new steps for LUIS call:

Open createorder_dialog.py add 2 new steps act_step and completeorder_step after order_step. Declare these new steps in the waterfall dialog in init function. Now we have to define these functions.

In act_steps, we will add code to accept the order description provided by the user. Pass this text to LUIS by calling getLuisResponse function of luisApp module. Now iterate through the “order” entities returned by luis. For each sub-order (or order entity) create an orderDetails object. Save the attributes/entities details obtained from Luis in the orderDetails object. Append this object in the orders_list. Add orders list in the context.

In completeorder_step start a new sub-dialog completeorder_dialog to check the completeness of each sub-orders.

We will create the new sub-dialog script after completing all the changes required in createorder_dailog.

We need to update summary_step as well. In the summary step we will add code to fetch the orders_list from the context object. Loop throgh each row and form the order description for each ro wby concatenating item name, quanity, flavour and size values.

Below is the updated createorder_dialog.py:

Add new sub-dialog completeorder_dialog:

Create a new dialog module in oprations folder. Add 4 waterfall steps — flavour_step, quantity_step, size_step and summary_step.

In the flavour step fetch the orders_list from the context. check if for the current_order (this attribute is set to 0 in createorder_dialog) index in orders_list, item_flavour attribute has value or not. If not then prompt user to provide the flavour name for the requested item. Otherwise proceed to the next step.

In the quantity_step fetch orders_list from context. Now check if any value is provided by the user, if yes then set the value as flavour for the the current_order in orders_list. Proceed to check if quantity value is provided or not. If not provided then prompt for the quantity else proceed to the next step.

In the size_step check if any new value is provided by the user, if yes then set it as quantity for the current_order in orders_list. Since size attribute is applicable for only item cake so this value is required only if the item_name is cake. Now check if the current_order item_name is Cake then check the size value, if provided then proceed to the next step else ask user to provide the size.

In the summary_step, check if any new value is provided by the user then set it as size value for the current order in orders_list. Now increase the current_order by 1. If current_order is greater than the length of the orders_list that means we have reached the end of the list hence end the dialog and go back to the createorder_dialog. Else continue completeorder_dialog from the first step.

Below is the code for completeorder_dialog:

Update App.py as we have added new sub-dialog:

With this we have completed coding for this part. In the next part we will complete view order and cancel order flow. We will now test the app using Bot Framework emulator. Refer to Part-1 for steps to run emulator.

Important links:

Source Code — https://github.com/sushmita-mishra/e-comm-chatobot-luis

Part-1 — https://medium.com/@sushmita_mishra18/create-chatbot-using-bot-framework-sdk-and-luis-part-1-e42c26d60366

Part-2 — https://medium.com/@sushmita_mishra18/create-chatbot-using-bot-framework-sdk-and-luis-part-2-5bf9f72f4a6a

Part-3 — https://medium.com/@sushmita_mishra18/create-chatbot-using-bot-framework-sdk-and-luis-part-3-8b479aa16e6d

Don’t forget to give us your 👏 !

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Create chatbot using bot framework sdk and LUIS — Part 4 was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Create chatbot using bot framework sdk and LUIS — Part 3

In Part-1 and Part-2 we created a basic e-comm chatbot and started creating ‘create order’ conversation flow. In this part we will be generating order id and saving the order details in data storage. In next part we will be adding intelligence using LUIS. Complete source code is here.

Following tasks we will complete in this part:

• Generate order ID for the order details provided by the user
• Create a csv file to save the order details against the user id. (to keep this tutorial simple we r using csv for data storage, replace it with your database).

Create a csv file to store order details:

In the project folder add a new folder. Name it Data. Create a csv file order_dummy.csv with 5 columns — user_id, order_id, creation_date, order_status, order_description.

Create helper functions to manage order data:

Create a new python script orderApp.py in the project folder. We will be adding 3 functions — addOrders, viewOrders, cancelOrder. addOrders function is for adding the order details in csv, viewOrder function is for fetching order details of a given user id and cancel order is to update the order status to cancel for the given user id.

Copy below code in orderApp.py:

Update createorder_dialog to generate order id and save order details:

Open createorder_dialog.py, we will now update summary_step. Add code to generate a new order id. All order id’s will have prefix ‘ord’ followed by 4 digit random number.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Create a pandas dataframe. Add a record with the order details along with user id and order id. also set the order status to “Order Received”. Pass this dataframe to addOrder function of orderApp module.

Add bot response, show the newly generated order id to the user in the bot response.

Below is the updated createorder_dialog.py:

With this we have completed coding required for Part-3. Test your bot using bot frmaework emulator. Follwo the steps givin in Part 1, to run the emulator.

Try similar flow with existing user. Check the data saved in csv file.

See you in next part of the blog.

Important links:

Source code is here. Part-1 of the blog. Part -2.

Don’t forget to give us your 👏 !

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Create chatbot using bot framework sdk and LUIS — Part 3 was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Greetings! My gf is working on a chatbot project and is responsible for designing the phrases and its behaviours using the Botpress (https://botpress.com/) platform for WhatsApp API. Emojis usually appear in any circumstances with a simple copy-paste, but this does not happen when we talk about lists. I’m posting as images two chatbot examples: the first one being the Netflix WhatsApp chatbot which has no issues at all while using emojis inside lists. The second, being her chatbot which simply refuses to display any pasted emojis in listings. We already tried several things such as using the emoji’s Unicode but no success so far. Any tips? As described, a simple copy-paste of the emojis themselves work just fine in any part of the bot, such as buttons, regular texts etc. Thanks a lot! ​ ​ Netflix bot ​ ​ Her bot submitted by /u/WilliamBenseny [link] [comments]

submitted by /u/winky334
[link] [comments]

submitted by /u/smbale [link] [comments]

submitted by /u/hasoni1111
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WhatsApp and Haptik have always believed in the power of messaging. Until now, WhatsApp was looked at as a customer service channel that can be used to resolve customer grievances.

With recent developments, it is clear that WhatsApp wants to enable Commerce for millions of businesses. Big brands are taking Commerce to where their users are engaging the most — WhatsApp. This has led to deeper engagements in a plethora of commerce options available to a user today.

WhatsApp commerce has been the engine behind the growth of many small and large-scale companies. WhatsApp chatbot has evolved into a full-blown channel that brands leverage to sell and transact with their customers, emerging as the most efficient channel to buy and sell digitally.

WhatsApp commerce is designed in such a way that it delivers a more personalized and delightful shopping experience for your customers on WhatsApp. We are confident that the convenience of shopping on WhatsApp chatbot is an experience that your customers will be delighted with.

A glimpse of what the next Commerce revolution looks like

Driving Discovery and Opt-in for WhatsApp

Now that you’re introduced to WhatsApp commerce let’s understand how your brand can get started with a WhatsApp chatbot. The first and most crucial aspect to consider at this stage is driving visibility and discovery. The two key questions to ask here are: How will your customers know that you are available on WhatsApp? How will they opt-in to receive your services over WhatsApp?

A key point to note is that WhatsApp requires a brand to get an opt-in from a user before it can start engaging with them, i.e., the user needs to consent to start a conversation with your business.

Let’s look at some techniques you can use to begin your brand’s conversations with customers on the world’s largest messaging channel.

1. SMS: You already have access to your customer’s phone number. You’re probably sending out SMS campaigns to them. This is a great way to get customer opt-ins.

2. Website: Your website is probably the most critical asset for your business. It’s where customers visit to do business with you. Here are a few sections that can have a customer opt-in banner:

• Contact us page
• Home page
• Header, footer, floating element
• Conversation starter button

The easier it is to find an entry point into WhatsApp on a website, the easier it is to start a conversation. Tapping a link or scanning a QR code is faster and easier than manually typing in a phone number.

The more prominent the placement, the more likely people will choose WhatsApp to engage with your business.

3. Telephony: You can leverage opt-in via telephone in two ways:

• IVR Deflection: This should be used during peak hours with the highest wait times. Simply add a “move conversation to WhatsApp” option to your IVR menu.
• Missed Call: Missed call automation is widespread in India. Hence this is another seamless way to get customer opt-ins. A user can simply give a missed call to your virtual number to get started!

4. Social Media: Your customers are likely using any or all social media channels to stay connected with their friends and family. This provides you an opportunity to get customer opt-ins where they’re spending most of their time. Let’s look at how you can leverage organic and paid social media to drive opt-ins.

Organic social media:

• Account bio/description: This placement is permanent and will not get lost in the stream of feed or stories.
• In-feed post: You can include the link in social media posts. Organic Instagram posts cannot contain links, so users must dial the number manually. In-feed posts have relatively short, in-feed visibility as they get quickly buried among other posts.
• Instagram profile page story highlight: Highly visible, as they are pinned to the top of your business’s profile. Once in the story, users swipe up to start a conversation.

Paid social media:

• Facebook & Instagram ads that click to WhatsApp: One tap/click: “Send Message” button will open WhatsApp conversation.
• Influencer social media post: If the influencer promotes your business on Facebook, Twitter, LinkedIn, etc., they can use a link. Instagram posts cannot contain links so that influencers can mention the number, and users can dial the number manually.

5. Traditional Advertising:

• QR code scan on OOH advertising
• Search engine ads
• PR/media news article

Now that we’ve understood the different techniques you can use to make customers aware of your WhatsApp presence, let’s look at how you should select the channels that make the most sense. The process of arriving at the solution includes answering four critical questions:

1. Where should we place entry points?

You should always add entry points to WhatsApp where your customers engage with your brand. E.g., If your target market is young people, ensure that you’re leveraging social media to drive opt-ins.

2. What behavior do we want to encourage?

E.g., If you’re going to answer product questions to close sales, create a “Click to WhatsApp” campaign and entry points on product pages.

3. What call to action should we include?

Always let customers know why they should start a WhatsApp conversation with you. Here are just a few:

• Have questions? We have answers! Click to chat.
• Can’t decide? Ask us! Message us on WhatsApp at 90009–90009
• Need help but hate waiting on hold? Click to message us on WhatsApp.

We strongly recommend A/B testing to determine the best approach for your brand.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

4. How can we provide context-aware responses?

Deep links automatically trigger a pre-populated message rather than simply opening a WhatsApp thread. You can use deep links to delight users with context-aware responses that focus on their specific needs.

WhatsApp has made it highly flexible for you to get customer opt-ins. Practically, you can use every communication channel to get customer opt-ins. Now, let’s understand how your brand can leverage marketing notifications to nudge your buyers ahead in the buying journey.

Redefining Customer Engagement with marketing notifications

People want to hear from their favorite brands in more ways across the customer journey. Companies can now send more types of proactive notifications to people that opt-in to receive them. They will now be able to send updates such as:

• Product recommendations related to a customer’s previous purchases
• Helpful informational alerts and reminders
• Relevant offers
• Newsletters

Why is this a game-changer for customer engagement?

Expected: People have already opted-in to receive this information from the business over WhatsApp, so they are not surprised when they message them.

Relevant: The messages are personalized to the specific person (Ex: based on recent purchases or recent engagement with the business, personalized content in the message itself).

Timely: People receive these messages when they are relevant.

Marketing notifications are an essential part of the WhatsApp commerce solution. Brands that understand the importance of providing a superior buying experience to their customers will be more likely to leverage these types of notifications to their full potential. Let’s dive into the most exciting aspect of the WhatsApp commerce journey.

The shopping experience on WhatsApp

The eCommerce landscape in India has evolved exponentially. The eCommerce revolution spearheaded by Amazon and Flipkart disrupted how traditional Indian retailers imagined shopping. India’s most prominent retailers had to adapt to the changing needs of their customers. The COVID-19 pandemic has further boosted the demand for quick and convenient shopping experiences.

As the eCommerce industry continues to roar, there has been steady growth in the Conversational Commerce landscape in India. WhatsApp chatbot has become an integral part of every brand’s digital transformation strategy. Let’s look at WhatsApp’s newly launched UI elements that are set to revolutionize shopping.

Multi-product messages

Multi-product messages contain a selection of up to 30 items from a business’ inventory. Allow your users to shop conversationally. Multi-product messages let your user navigate to a specific category. You can also send personalized offers and recommendations to re-order their regular take-out order of fewer than 30 items.

Single-product messages

Single product messages are messages with a single product item from the business’ inventory. The product is displayed in a Product Detail Page (PDP) format.

Single Product Messages are best for guiding customers to one specific item from a business’ inventory.

You can also offer quick responses from a limited set of options, such as:

• Responding to a customer’s specific request
• Providing a recommendation
• Reordering a previous item

Shopping Cart Experience

After viewing products, a customer can add them to their shopping cart and send that cart to a business.

For commerce on WhatsApp, a shopping cart is unique to a person/business chat thread in a specific device and has no expiration date.

Customers can add up to 99 units of each catalog item to a shopping cart, but there is no limit on the number of distinct items added to a cart.

List Messages & Quick Reply Buttons

List Messages presents a choice of up to 10 alternatives for users to pick from in a menu-type view. This makes it easier for people to choose from a list of alternatives.

Customers can now select an option from a list of Quick Reply Buttons rather than type back complete answers from a list of alternatives. This feature makes the entire conversation journey significantly faster. Read more about List Messages & Quick Reply Buttons.

Why You Should Use It

Both Multi and Single Product Messages lend themselves best to user experiences that are simple and personalized, where it’s a better experience to guide the customer to a subset of items most relevant to them, rather than browsing a business’ complete inventory.

For a user speaking on a messaging channel like WhatsApp, Multi-Product Messages & Single Product Messages make the journey of discovery and engagement easier. What’s more, these can be customized to a user’s preference than them having to browse through an entire catalog.

Simple & Efficient

Combining the features with navigation tools like NLP, text search, or List Messages and Reply Buttons to get what the customer is looking for fast.

Personal

It is populated dynamically so it can be personalized to the customer or situation. For example, you can show a Multi-Product Message of a customer’s most frequently ordered items.

Business Outcomes

A performant channel for driving orders, during testing, businesses had an average 7% conversion of Multi-Product Messages sent to carts received.
AI-Powered Recommendation Engine

AI-powered Recommendation Engine

Imagine this: The salesperson in your store is trained to judge the visitor’s intent and recommend products based on intent and past visit behavior. Well, we’ve made this a reality. Haptik’s AI-powered recommendation engine increases upsell and cross-sell opportunities by contextually suggesting products your user needs based on their intent and past behavior. Haptik’s Recommendation Engine automatically matches user requirements with your product catalogs, description, customer reviews, and other data to help you convert passive visitors into engaged buyers.

Payment on WhatsApp

The frictionless experience of digital payments has accelerated the adoption of cashless transactions. Right from retail outlets in malls to local Kirana shops, the UPI code scanner sits comfortably at the cash counter of every shop. As your customers have grown accustomed to digital payments, their expectations and demands have increased rapidly.

This is where our 3-click payment experience on WhatsApp chatbot helps. WhatsApp will allow your customers to initiate payments from WhatsApp while on the purchase journey. The best part? They don’t have to navigate between multiple apps. You can also use notifications as payment reminders and ensure a frictionless payment collection process.

Conclusion

WhatsApp commerce is poised to become the biggest game-changer in the eCommerce story of India! Customers don’t have to browse through multiple apps and websites to find what they’re looking for. WhatsApp will become the most important channel when it comes to generating sales and unlocking hyper exponential growth.

Haptik is proud to be the first BSP to launch these new features with JioMart. The use cases that JioMart is driving with WhatsApp commerce are:

• Driving conversations with marketing notifications
• Sending personalized product recommendations based on customer’s needs & past behavior
• Driving orders through interactive messages and a seamless Shopping Cart experience

Haptik has also powered WhatsApp solutions for some of India’s most prominent brands such as Jio, TATA, HDFC, Dream11, OLA, Kotak Mahindra, etc. We’re a recipient of 15 G2 badges and recognized by Gartner and Opus Research. With handling over 4+ Billion interactions, we’re India’s largest Conversational AI company!

Did we mention that we have built the World’s largest WhatsApp chatbot for MyGov India?

Join the WhatsApp Commerce Revolution

GET IN TOUCH

Don’t forget to give us your 👏 !

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Reimagining the future of Commerce with WhatsApp was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Create chatbot using bot framework sdk and LUIS — Part 2

In part 1 we completed basic structure of the bot service. In Part-2 our bot will give create, view and cancel options to the user.

This is continuation of Part-1. If you are coding along with me then follow this blog otherwise you can also download the completed code from here.

In Part-2 we will cover following:

• Our Bot will give Create Order, View Order and Cancel Order options to the user.
• We will add sub-dialogs for create, view and cancel.
• For create order we will complete the basic flow of conversation.
• For create order selection, conversation will be diverted to create order sub-dialog.
• Bot will ask user to provide the order details.
• Bot will then respond with the same order details (echo) provided by the user.

In the subsequent parts we will save the order details against the user id in a data storage. we will add intelligence to the bot with LUIS service to interpret the order description.

Creating sub-dialogs

Create a new folder in dialogs folder and name it operations. In operations folder create 3 scripts- createorder_dialog.py, vieworder_dialog.py and cancelorder_dialog.py

Let us start with coding createorder_dialog.py. In this part we will add only 2 steps (order_step, summary_step) in the waterfall dialog steps for create order. we will add more steps in the subsequent parts.

In the first step bot asks user to provide his order details. Second step is added to accept the text provided by the user in the context object and echo i.e. respond with the same text as bot’ response. in the next part we will save the order in the data storage. But for now just echo the text.

Add below code in createorder_dialog.py

We will also define the classes vieworder_Dialog and cancelorder_dialog. We will not add any waterfall steps for now. just the basic sturcture.

Code for vieworder_dialog.py:

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Code for cancelorder_dialog.py

Now we need to update app.py to create objects of the new dialog classes we have created. These objects will be passed to the main_dialog class.

Below is the updated app.py:

Next we will update main_dialog.py. First update the init function to add parameters to accept objects of the sub-dialog classes. These objects needs to be added as sub-dialog. Call add_dialog method to add these objects in the dialog. In the waterfall conersation flow we will add one more step act_step after intro_step.

update intro_step to give Create order, View order and cancel order options to the user. In act_step add code to read the option selected by the user. Depending on users selection divert the dialog to createorder_dialog, vieworder_dialog or cancelorder_dialog.

Update main_dialog.py:

We have completed coding required for Part-2. In the next part we will add code to save order details provided by the user in a data storage. In the subsequent parts we will add more intelligence to the Bot to read and interpret the order details.

Now test the bot using Bot framework emulator. instructions for using emulator is already given in Part-1.

To be continued..

Don’t forget to give us your 👏 !

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Create chatbot using bot framework sdk and LUIS — Part 2 was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.