Conversational UI

Introduction

With the Conversational UI module, you can create a GenAI-based chat user interface. It contains the needed data model, pages, snippets, and building blocks. You can integrate with any LLM and knowledge base to create your full-screen, sidebar, or modal chat. It integrates with the Atlas framework and is the basis for the AI Bot Starter App. It is also included in the Blank GenAI App and the Support Assistant Starter App.

Mendix has produced a Conversational AI Design Checklist which suggests some best practices for introducing conversational AI into your app.

Typical Use Cases

Typical use cases for Conversational UI include the following:

• Create a chat interface for users to chat with Large Language Models (LLM).
• Allow users to switch between different implementations by switching providers.
• Include advanced capabilities to control the model’s behavior, for example, by setting the temperature parameter.
• Easily extend the chat interface with advanced concepts, such as RAG or the ReAct pattern. For more information, see Using Generative AI.

Features

The Conversational UI module provides the following functionalities:

• UI components that you can drag and drop onto your pages, for example:
  • Layouts to have a sidebar or floating pop-up chat
  • Pages that you can use in your navigation for chat
  • Snippets that you can use directly on your pages, for example to display messages or a history sidebar
  • A floating button for opening a pop-up chat
  • Pages, snippets, and logic to display and export token usage data (if enabled in GenAI Commons and supported by the GenAI Connector of choice)
• Operations to set up your context, interact with the model, and add the data to be displayed in the UI
• Domain model to store the chat conversations and additional information
• Integration with any model that is compatible with GenAI Commons

Limitations

This module is intended to enable chat interactions between a user and an AI model. It is not designed for conversations between two human users.

Prerequisites

To use the Conversational UI module, your Mendix Studio Pro version must be 9.24.2 or higher.

You must also ensure you have the prerequisite modules that Conversational UI requires. The modules are included by default in the Blank GenAI App, the AI Bot Starter App, and the Support Assistant Starter App, otherwise you must install them yourself:

• Atlas Core
• Data Widgets
• GenAI Commons
• Nanoflow Commons
• Web Actions

Finally, you must also install and configure a connector that is compatible with GenAI Commons. Mendix provides platform-supported integration with either (Azure) OpenAI or Amazon Bedrock. You can also integrate with custom models by making them compatible with the GenAI Commons Request and Response.

Installation

Follow the instructions in How to Use Marketplace Content to import the Conversational UI module into your app.

Configuration

To use Conversational UI in your app, you must perform the following tasks in Studio Pro:

1. Add the relevant module roles to the user roles in the project security.
2. Create the UI for the chat in your app by using the pages and snippets as a basis.
3. Make sure there is a chat context available on the page where the conversation should be shown.
4. Associate one or more provider-configs to the chat context.
5. Use a default action microflow or create a custom flow that will be executed when the user clicks the Send button.
6. In the project theme settings, include the ConversationalUI module in the right order. Add it after Atlas_Core so the styling does not get overwritten (see Ordering UI Resource Modules for more information).
7. Optionally, customize styling by overwriting variables and adding custom scss. Custom styling modules need to be loaded after ConversationalUI when ordering UI resources.

The main entities are shown for reference in the diagram below. For technical documentation, follow the steps in the Technical Reference section.

Configuring the Roles

Make sure that the module role User is part of the user roles that are intended to chat with the model. Optionally, you can grant the _addOn_ReadAll role to admin roles, so that users with that role can read all messages. A role for usage monitoring is related only to the Token monitor snippets and does not relate to the conversational snippets.

Module role	Description
User	Role needed for every user that should be able to interact with the chat components. Users can only read their messages (and related data).
_addOn_ReadAll	Role can be granted additionally. Users with both roles can read all chat data.
UsageMonitoring	Can view and export all token usage data. This is related to a module role with the same name in the GenAI Commons module.

Creating the Chat UI

A set of reusable pages, layouts, and snippets is included in this module to allow you to add the conversational UI to your app.

Pages and Layouts

You can include the following pages in your navigation, or copy them to your module and modify them to suit your use case:

• ConversationalUI_FullScreenChat - This page displays a centered chat interface on a full-screen responsive page.
• ConversationalUI_Sidebar - This page displays the chat interface on the right side with the full height.
• ConversationalUI_PopUp - This is a floating pop-up in the bottom-right corner. To open it, users can click the Snippet_FloatingChatButton that floats in the bottom-right corner. Alternatively, you can use the building block Floating Chat Button from the toolbox to create your custom opening logic.

All pages expect a ChatContext that needs to have an active ProviderConfig. The user can chat with the LLM on all these pages, but cannot configure additional settings, such as the model or system prompt. There are many ways to enable this: on a custom page before the chat was opened, on a custom version of the chat page itself, or in the action microflow that is stored in the active ProviderConfig.

Snippets

Drag the following snippets onto your other pages to quickly build your version of the chat interface.

Chat Interface Snippets

Chat interface snippets contain the whole chat history as a list view and below that a text area for users to enter the user prompt. Some UI components show an error message when a call has failed, or that show progressing loading bots when waiting for the response. When a user clicks the Send button, the action microflow is executed.

The following versions are available and can be swapped as needed:

• Snippet_ChatContext_ConversationalUI - This snippet shows both the user messages and the responses on the left side of the container.
• Snippet_ChatContext_ConversationalUI_Bubbles - This snippet shows the user messages on the right side and the responses on the left side, similar to common chat apps. The content is placed inside colored cards (bubbles).

If the snippet does not fit your use case, you can inline the snippet to customize it to your needs.

Message Snippets

The message snippets are already part of the Chat Interface Snippets but can be used individually in your custom setup if needed. They contain the content of a single message, for example, to be used in a list view.

The following versions are available and can be swapped as needed:

• Snippet_Message - This snippet shows both the user messages and the responses on the left side of the list.
• Snippet_Message_Bubble - This snippet shows the user messages on the right side and the responses on the left side, similar to common chat apps. The content is placed inside colored cards (bubbles).

Advanced Configuration Snippets

The following additional snippets can be used to give the user more control over the chat conversations.

• Snippet_ChatContext_AdvancedSettings - This snippet can be placed on pages to let users configure specific parameters (current temperature). Use the microflow AdvancedSettings_GetAndUpdate to set the boundaries and default value for advanced settings in the UI.
• Snippet_ChatContext_SelectActiveProviderConfig - With this snippet, users can select an active Provider Config from all associated configurations, for example, to let them select a model.
• Snippet_ChatContext_HistorySideBar - This snippet can be used in a list view to show past conversations. It displays the topic of the chat context as well as a delete icon on hover. For details on how to set the topic, see ChatContext operations.

See the AI Bot Starter App or the GenAI Showcase App on how to use those snippets.

Providing the ChatContext

The ChatContext is the central entity in the pages and snippets above and represents a chat conversation with potentially many messages. It functions as the input for the action microflow executed when the user clicks the Send button. It can only be viewed by the owner (see Module Roles for exceptions). The object needs to be created for every new chat conversation and comprises the messages sent to and received from the model during a chat interaction. A ProviderConfig should be associated via ChatContext_ProviderConfig_Active to execute the correct action microflow. To do this in an ACT microflow that opens the chat page, see the USE_ME > Pages folder for more examples.

If you need additional attributes or associations on the ChatContext, use a specialization or an extension entity that refers to the object which can then be retrieved and altered when needed (for example in the action microflow). The AI Bot Starter App shows an example of the extension entity approach.

Chat Context Operations

Depending on the implementation, you can create this entity using a microflow that opens the page or using a datasource microflow on the page itself. The following are the operations in the toolbox for creating the ChatContext:

• New Chat creates a new ChatContext and a new ProviderConfig (or specialization of such depending on the input). The ProviderConfig is added to the ChatContext and set to active. Additionally, the action microflow of the new ProviderConfig is set.
• New Chat with Existing Config creates a new ChatContext and sets a given ProviderConfig to active.
• New Chat with Additional Configs creates a new ChatContext, adds a ProviderConfig to the ChatContext, and sets it to active. In addition, a list of ProviderConfig can be added to the ChatContext (non-active, but selectable in the UI).

SuggestedUserPrompt

It is possible to add suggested user prompts to a ChatContext. They appear as a button for new chats. When a user clicks the Suggested User Prompt that appears above the chat input box, the action microflow sends the content of the prompt to the model and starts a predefined chat in the interface. New SuggestedUserPrompts can be created and added to a ChatContext with the Add SuggestedUserPrompt toolbox action.

Associating the ProviderConfig

The ProviderConfig contains the selection of the model provider with which the AI Bot can chat. It also refers to an action microflow that is executed when the Send button is clicked for a ChatContext that has the ProviderConfig associated.

A ProviderConfig (or specialization) can be added directly using the aforementioned operations that create a new ChatContext.

If the ChatContext however already exists and a new ProviderConfig needs to be added, use the New Config for Chat toolbox action. The ProviderConfig can optionally also be set to active if IsActive is set to true. Additionally, the action microflow of the new ProviderConfig is set.

ChatContext_AddProviderConfig_SetActive is the counterpart of this flow when both the ChatContext and the ProviderConfig exist already.

Defining and Setting the Action Microflow

The Action Microflow that is stored on a ProviderConfig is executed when the user clicks the Send button. This microflow handles the interaction between the LLM connectors and the Conversational UI entities. The USE_ME folder included in the Conversational UI module contains example action microflows for both OpenAI and Amazon Bedrock. You can copy these microflows and modify them for your use cases, or use them directly for test purposes.

Add the action microflow to an existing ProviderConfig by using the Set Chat Action toolbox action. Note that this action does not commit the object, so you must add a step to commit it afterward.

Creating a Custom Action Microflow

A typical action microflow is responsible for the following:

• Convert the ChatContext with user input to a Request structure for the chat completions operation.
• Retrieve the connection details (i.e. credentials, configuration, etc.) for the operation.
• Execute the chat completions operation for the LLM of choice.
• Update the ChatContext structure based on the response so that the user can see the result in the UI.

If you want to create your custom action microflow, keep the following considerations in mind:

• Only one input parameter of ChatContext or a specialization is accepted.
• The return type needs to be a Success Boolean.
• Use the chat context and request operations to facilitate the interaction between the chat context and the model.
• The custom action microflow can only be triggered if it is set as an action microflow for the ProviderConfig using one of the operations mentioned before.

ChatContext operations

The following operations can be found in the toolbox for changing the ChatContext in a (custom) action microflow:

• Set ConversationID sets the ConversationID on the ChatContext. Storing the ConversationID is needed for a chat with history within Retrieve and Generate with Amazon Bedrock.
• Set Topic sets the Topic of the ChatContext. This attribute can be used in the History sidebar while making historical chats visible to users.

Request Operations

The following operations are used in a (custom) action microflow:

• Create Request from ChatContext creates a Request object that is used as an input parameter in a Chat with History operation as part of the action microflow. For more information about the Chat with History operation, see (Azure) OpenAI or Amazon Bedrock.
• Get Current User Prompt gets the current user prompt. It can be used in the action microflow because the CurrentUserPrompt from the chat context is no longer available.
• Update Assistant Response processes the response of the model and adds the new message and any sources to the UI. This is typically one of the last steps of the logic in an action microflow.

Customizing styling

The ConversationalUI module comes with stylesheets that are intended to work on top of Atlas Core. You can use variables and custom classes to modify the default rendering and think of colors, sizes, and positions. To learn more about customizing styling in a Mendix app in general and targeting elements using SCSS selectors, refer to the how-to page.

Variables

The following variables have a default value defined in the Conversational UI module. You can override the values by setting a custom value in the _custom-variables.scss file or your styling module.

Variable name	Description
chat-width	the max-width of the chat UI in a full-page setup
send-btn-size	the height and width of the button in the user chat input box
chat-input-max-height	the max-height of the user chat input box
chat-header-color	the background color of the topbar of the pop-up and sidebar chat window
pop-up-chat-bottom-position	the absolute bottom-position of the pop-up chat window
pop-up-chat-right-position	the absolute right-position of the pop-up chat window
pop-up-chat-width	the width of the pop-up and sidebar chat window
pop-up-chat-height	the height of the pop-up chat window
chat-bubble-user-background	the background color of a user message in the pop-up and sidebar chat
chat-bubble-assistant-background	the background color of an assistant message in the pop-up and sidebar chat

You can find the default values of these variables in the _chat-variables.scss file that is shipped with this module.

Creating Custom SCSS

You can use the following classes in your custom stylesheets to overwrite the default styling of Conversational UI and modify the behavior of the chat elements in your app.

Class name	Target element
btn-chat-popup	the floating button that opens the pop-up chat, also see Snippet_FloatingChatButton
chat-container	the container around the chat, including the input box and messages
messages-container	the container around the messages inside of chat-container
send-btn	the button in the user chat input box
chat-btn-suggested-prompt	a suggested prompt for the user to click instead of typing
chat-input-wrapper	the container around the user chat input box
user-input-instructions	the additional information text below the user chat input box
message--assistant	an assistant message in the conversation
chat-bubble-wrapper--assistant	an assistant message in the pop-up and sidebar chat
message--user	a user message in the conversation
chat-bubble-wrapper--user	a user message in the pop-up and sidebar chat

Token Monitor Snippets

A separate set of snippets has been made available to display and export token usage information in the running application. This is applicable for LLM connectors that follow the principles of GenAI Commons and as a result store token usage information. The following snippets can be added to (admin) pages independently from the conversation logic described in earlier sections.

• Snippet_TokenMonitor - This snippet can be used to display token usage information in charts and contains several other snippets that you can use to build your token monitor dashboard. To display the token usage data, users will need the UsageMonitoring user role.
• Snippet_TokenMonitor_Export - This snippet can be used to display token usage information in a grid and export it as .xlsx.

Technical Reference

The module includes technical reference documentation for the available entities, enumerations, activities, and other items that you can use in your application. You can view the information about each object in context by using the Documentation pane in Studio Pro.

The Documentation pane displays the documentation for the currently selected element. To view it, perform the following steps:

1. In the View menu of Studio Pro, select Documentation.

2. Click the element for which you want to view the documentation.

   

Troubleshooting

This section lists possible solutions to known issues.

Cannot Export Usage Data for the Token Monitor

The export of usage data for the token monitor does not work correctly.

Cause

The Data Widgets module that you have installed is in an older version which does not support exporting data to .xlsx format from the Data Grid 2 widget.

Solution

Update the Data Widgets module to version 2.22.0 or above.