Implement multi-step conversations

The advent of AI and Machine Learning technologies has revolutionized the way we interact with systems. Businesses all over the world are leveraging these technologies to streamline their customer operations and harness process efficiency. One such technology is the use of chatbots to maintain customer interaction. However, one hurdle that has plagued the efficacy of chatbots is their inability to maintain multi-step conversations. This is where Microsoft Azure steps in, offering an AI-102 exam that is dedicated to designing and implementing Microsoft Azure AI solutions, including creating AI that can handle complex, multi-step conversations.

Understanding Multi-step Conversations

A multi-step conversation is a conversation that comprises of multiple turns, where each turn is a single exchange between the user and the bot. These conversations typically involve a bot initiating a conversation with a user, the user responding, and then the bot providing another response based on that user’s response.

With the Azure Bot Framework, designing and implementing multi-step conversations have become a breeze. This framework provides a comprehensive platform to build, test, deploy, and manage AI-powered chatbots that can carry multi-step conversations.

Implementing Multi-step Conversations

Implementing multi-step chatbot conversation using Azure involves a systematic approach, such as designing dialogs, conversation flow, entity extraction, and turn context handling.

• Dialogs:

  Dialogs are fundamental building blocks for creating bots. They act as a conversational context, encapsulating conversational process flow. Dialogs in Azure’s bot framework offer complete control over how bots handle a conversation.

  The Bot Framework provides several classes derived from the Dialog class, like WaterfallDialog, ComponentDialog, etc. The WaterfallDialog, for instance, allows the bot to walk through a series of steps as per the sequence in which they are added.

  Example code:

  WaterfallDialog(
"bookFlight",
[
this.askDestinationStep,
this.askDateStep,
this.confirmStep
]
)

• Conversation Flow:

  The conversation flow involves defining and managing the sequence of steps in a conversation, and this is where the TurnContext object is used. It manages activities such as receiving input and creating responses.

• Entity Extraction:

  Entity extraction is crucial to understand the user’s intent. Microsoft’s natural language understanding service, LUIS (Language Understanding Intelligent Service), provides features that help in identifying the entities from the user’s input.

• TurnContext Handling:

  The Azure Bot framework uses the concept of a turn in a conversation, where each turn consists of an input from the user and an output from the bot. The TurnContext object provides information about the incoming activity, such as the message text and user id.

Maintaining State In Conversations:

To engage in multi-step conversations, a bot needs to maintain and track the context of the conversation. In the Azure Bot Framework, each conversation with a user will have a conversation state and a user state. The conversation state tracks the conversation history, and the user state tracks the specific user information.

ToList is an in-memory storage provider, but for production-level bots, you would use Azure Blob Storage, Azure Cosmos DB, or other storage services.

In conclusion, the implementation of multi-step conversations has now become a tangible reality with Microsoft Azure’s AI solutions. By mastering these concepts and features, developers can design an effective chatbot to handle complex user interactions.

Remember, even the most intricate bot conversation can be broken down into smaller, manageable fragments. With the right planning and following the approach of divide-and-conquer, the task becomes less intimidating. The AI-102 certification will undoubtedly equip you with the necessary knowledge and skillset to implement such advanced features, bolstering your capacity as a professional in the AI domain.

Practice Test

1) True or False? Multi-step conversations in a chatbot are implemented using waterfalls in Azure Bot Framework.

• True
• False

Answer: True

Explanation: In the Azure Bot framework, the waterfall model enables you to structure the bot to “listen” and “respond” by leading the user through a series of steps or dialogues.

2) Which among the following services of Azure are primarily used to implement a multi-step conversation?

• A) Azure Bot Services
• B) Azure Functions
• C) Azure Logic Apps
• D) Azure Web App

Answer: A) Azure Bot Services

Explanation: Azure Bot Services provides a crafted environment and tools for developing, testing and publishing intelligent bots that interact naturally with users.

3) True or False? Azure bot service does not allow you to integrate your bot with multiple channels.

• True
• False

Answer: False

Explanation: The Azure bot service actually includes features which allow integration of bots with multiple channels, such as Teams, Email, and more.

4) The ___________property of a prompt options object is used to specify the initial message prompt in a multi-step conversation.

• A) prompt
• B) value
• C) choices
• D) resolver

Answer: A) prompt

Explanation: The ‘prompt’ property of a prompt options object contains the initial message that is sent to the user.

5) LUIS (Language Understanding Intelligent Service) helps in managing multi-step conversation in dialogue flow.

• A) True
• B) False

Answer: A) True

Explanation: LUIS helps the bot communicate with users in natural language and extract meaningful pieces of information from the conversation.

6) True or False? Multi-step conversations are not possible with the QnA Maker in Azure.

• True
• False

Answer: False

Explanation: The QnA Maker can be used in conjunction with other Azure services to aid in creating multi-step conversations.

7) ___________ is a method that adds a new Waterfall step to the dialog.

• A) .addDialog()
• B) .next()
• C) .add()
• D) .beginDialog()

Answer: C) .add()

Explanation: .add() is a method in the WaterfallDialog class which adds a new waterfall step in the dialog.

8) The adaptive card system in Azure Bot Service is beneficial for multi-step conversations.

• A) True
• B) False

Answer: A) True

Explanation: Adaptive Cards can be used in complex multi-step conversations by structuring the user responses.

9) What is the primary function of Dialogues in the implementation of multi-step conversations?

• A) They authenticate user data
• B) They maintain and manage the state of a conversation
• C) They analyze the sentiment of user inputs.
• D) They translate user inputs into different languages.

Answer: B) They maintain and manage the state of a conversation

Explanation: Dialogues are designed to manage and maintain the state of a conversation by keeping track of the user inputs at different stages of a multi-step conversation.

10) True or False? A prompt always returns the user’s entire message in a multi-step dialogue.

• True
• False

Answer: False

Explanation: A prompt does not return the user’s entire message. It returns whatever value that’s been recognized in the user’s input, defined by the type of the prompt.

11) A multi-step conversation can switch between intents within a dialogue.

• A) True
• B) False

Answer: A) True

Explanation: A dialogue can handle different intents and adapt the path of the conversation based on the user’s inputs which can vary significantly in a multi-step conversation.

12) Which Azure service aids multi-step conversation by recognizing entities in a conversation?

• A) LUIS
• B) Bot Framework Emulator
• C) Azure DevOps
• D) Azure Functions

Answer: A) LUIS

Explanation: LUIS (Language Understanding Intelligent Service) helps the bot interact with users naturally by extracting meaningful information like entities from the conversation, which is required in multi-step dialogues.

13) You can implement multiple Waterfall Dialogues in a single bot conversation.

• A) True
• B) False

Answer: A) True

Explanation: You can use multiple Waterfall Dialogues and switch amongst them based on certain conditions to effectively manage a multi-step conversation in a bot.

14) Is it possible to use both QnA maker and LUIS simultaneously in a multi-step conversation?

• A) True
• B) False

Answer: A) True

Explanation: You can use both LUIS and QnA maker simultaneously in multi-step conversation. While LUIS can handle complex intents, QnA maker can be used for simple question-answer scenarios.

15) The Waterfall Model in Azure Bot service is only applicable for simple, linear conversations.

• A) True
• B) False

Answer: B) False

Explanation: While the Waterfall Model assist in structuring a conversation, non-linear, complex workflows can also be managed by using multiple waterfall dialogues and conditional checks.

Interview Questions

What is a multi-step conversation in the context of Microsoft Azure AI?

Multi-step conversations in Microsoft Azure AI refer to the interactive dialogues in a chatbot that require multiple exchanges of information between the user and the bot. This could involve asking the user for more information, confirming the details, or even integrating with backend systems to execute user’s requests.

How is context important in multi-step conversations in Microsoft Azure AI?

Context refers to the relevant information that helps the chatbot understand the user’s intent and maintains the conversation flow. It includes data like user-provided information, system information, or previous interaction details. Context is crucial as it allows the bot to accurately respond in sequential conversational turns.

What is the role of a “Turn” in a multi-step conversation?

A “Turn” in a multi-step conversation represents each round of a user’s input and the bot’s response. A conversation is a series of turns between the user and the bot.

How can you maintain context across turns in a Microsoft Azure AI chatbot?

Microsoft Azure AI provides in-memory storage objects like “userState” and “conversationState” to maintain context across turns. These objects can store and retrieve values across a session.

What is the ‘Waterfall Model’ in Microsoft Azure Bot Framework?

The ‘Waterfall Model’ represents a sequence or a series of steps that the bot follows during a conversation. Each step in the waterfall corresponds to a function that gets executed in response to the user’s input. This model is useful for structuring complex or multi-step conversations.

How do you use the Prompt Dialog to implement multi-step conversations?

In the Microsoft Azure Bot Framework, the Prompt Dialog is used to ask users for input and then process the response. It can be used multiple times in sequence to implement multi-step conversations by asking for and validating multiple user inputs.

Why are Language Understanding (LUIS) models useful in multi-step conversations?

LUIS models are useful because they allow the bot to interpret user input in a more flexible and natural way, understanding the intent and extracting relevant entities to use in the conversation flow.

What is the purpose of the Dialog Stack in Microsoft Azure Bot Services?

The dialog stack manages the series of dialogs (which represents each interaction the bot has with the user) as a stack structure, giving control to the most recently added dialog. This helps managing complex, multi-step conversation flows, allowing for interruption and resumption.

How can ‘Adaptive Dialogues’ enhance multi-step conversations in Azure Bot Framework?

Adaptive Dialogues allow for dynamic and flexible conversations. They handle interruptions well and can manage various conditions and steps in the conversation based on contextual events, making them ideal for more complex, multi-step conversations.

What is a ‘Component Dialog’ in the Microsoft Azure Bot Framework?

A Component Dialog acts as a reusable module in the bot design. It encapsulates a dialog and related processing into a self-contained unit to create a piece of bot functionality that can be leveraged for multi-step conversations.

How is state management related to multi-step conversations in Azure Bot Services?

State management refers to how a bot keeps track of the context and user data over multiple turns. It’s crucial for multi-step conversations where the bot needs to remember previous inputs or commands to keep the conversation flowing.

What is .beginDialog() method in Azure Bot Service?

The .beginDialog() method is used to start a new dialog and push it onto the top of the dialog stack. It’s commonly used in multi-step conversations to initiate a new line of questioning or interaction.

How is ‘Dispatch tool’ used in Azure AI for multi-step conversations?

The Dispatch tool is used to consolidate multiple LUIS models and QnA services. It helps in routing the user’s query to the relevant bot with the correct answer or follow-up question, thereby facilitating multi-step conversations.

Why are ‘Validators’ important in Azure Bot Service for multi-step conversations?

Validators are important as they validate the user’s input before moving to the next step in the conversation. They prompt the user to re-enter their input until a valid response is received, ensuring accuracy and comprehension in multi-step conversations.

How does a ‘Channel’ affect multi-step conversations in Microsoft Azure AI?

A ‘Channel’ is the medium where the bot interacts with the user. Different channels may support different features or inputs, which can affect the complexity and functionality of multi-step conversations. They should be selected considering the bot’s conversational requirements, user’s experience, and supported functionalities.