Create Powerful Canvas Apps with OpenAI

Table of Contents

1. Introduction
2. Understanding Open AI and Microsoft Power App Canvas Application
3. Creating a Swagger Definition File for a Custom Connector
4. Exploring Completing and Chat Endpoints in Open AI
5. Building the Power App Canvas Application
6. System Initialization: Giving AI a Purpose
7. Building the User Interface for Asking Questions
8. Building the Payload Dynamically
9. Sending the Request to Open AI
10. Capturing and Storing the AI Response
11. Updating the Payload with User Conversations
12. Dynamically Changing Temperatures and Max Tokens
13. Fixing the Payload and Slider Issues
14. Testing the Chatbot Application
15. Conclusion

Introduction

In this article, we will dive deep into the implementation of Open AI inside a Microsoft Power App Canvas application. We will explore the concepts of completing and chat endpoints, and how they can be used to Create engaging chatbot experiences.

Understanding Open AI and Microsoft Power App Canvas Application

Before we Delve into the details, let's take a moment to understand what Open AI and Microsoft Power App Canvas applications are. Open AI is a powerful artificial intelligence platform that enables the development of language models and chatbots. Microsoft Power App Canvas applications, on the other HAND, are low-code development platforms that allow users to create and customize business apps without the need for extensive coding knowledge.

Creating a Swagger Definition File for a Custom Connector

To integrate Open AI into a Power App Canvas application, we need to create a Swagger definition file for a custom connector. This file will define the endpoints and operations that will be used to communicate with Open AI. We can customize the connector Based on our specific requirements, such as completions or chat functionalities.

Exploring Completing and Chat Endpoints in Open AI

In Open AI, there are two main endpoints that we will be exploring: completions and chat. The completions endpoint is used for generating text completions based on a given prompt. On the other hand, the chat endpoint allows for interactive conversations with the AI model. We will focus on the chat endpoint in this article, as it provides a more robust and engaging chatbot experience.

Building the Power App Canvas Application

Now that we have a basic understanding of Open AI and the available endpoints, we can start building our Power App Canvas application. The application will consist of a user interface where users can Interact with the chatbot by asking questions and receiving responses.

System Initialization: Giving AI a Purpose

Before we can start interacting with the AI model, we need to initialize the system by giving it a purpose. This is done through system initialization, where we define the role and purpose of the AI. By providing a clear purpose, we enable the AI to better understand and respond to user queries.

Building the User Interface for Asking Questions

Once the system is initialized, users can ask questions and receive responses from the chatbot. We will design a user interface that includes a text input field where users can enter their questions. The entered questions will then be sent to the AI model for processing.

Building the Payload Dynamically

To interact with the Open AI Chat endpoint, we need to build a payload that includes the user's input and other necessary parameters. We will dynamically construct the payload based on the user's input and other contextual information.

Sending the Request to Open AI

After the payload is constructed, we will send the request to the Open AI chat endpoint. This will initiate the conversation with the AI model and trigger the generation of a response.

Capturing and Storing the AI Response

Once the AI model generates a response, we need to capture and store it. We will update our user interface to display the response received from the chat endpoint. Additionally, we will store the conversation history for future reference.

Updating the Payload with User Conversations

As the conversation progresses, we need to update the payload with the user's questions and the corresponding AI responses. By keeping track of the conversation history, we can maintain contextual awareness within the chatbot.

Dynamically Changing Temperatures and Max Tokens

To enhance the chatbot's capabilities, we can allow users to control the temperature and maximum tokens settings. These settings influence the AI model's creativity and response length. We will implement sliders that dynamically update these parameters based on user input.

Fixing the Payload and Slider Issues

During the development process, we may encounter issues with the payload construction and slider functionality. We will troubleshoot and fix any issues to ensure the smooth operation of our chatbot application.

Testing the Chatbot Application

Before concluding our development process, it is essential to thoroughly test the chatbot application. We will engage in various conversations with the AI model to evaluate its responses and behavior.

Conclusion

In this article, we explored the implementation of Open AI inside a Microsoft Power App Canvas application. We discussed the importance of system initialization, payload construction, and capturing AI responses. By following the step-by-step guide, You can create your own AI-powered chatbot application. The possibilities are limitless, and with further exploration, you can enhance and customize your chatbot based on your specific requirements.