Create a Powerful Chat Bot with Azure OpenAI | .NET Conf 2023

Create a Powerful Chat Bot with Azure OpenAI | .NET Conf 2023

Table of Contents

• Introduction
• Building a Chatbot on Azure OpenAI using Net8
• Creating an Azure OpenAI Resource and API
• Differences Between Azure OpenAI and OpenAI
• Setting Up the Development Environment
• Creating a Chat Controller
• Creating an MVC Project with Visual Studio Code
• Working with Environment Variables and Packages
• Implementing Chat Functionality
• Extracting Text from a PDF
• Integrating the PDF Data into the Chatbot
• Limitations and Improvements
• Conclusion

Introduction

Welcome to NetCon! In this article, we will explore how to build a chatbot on Azure OpenAI using Net8. We will demonstrate how to make the chatbot work with GBD4 and integrate data, specifically a PDF file, to enable the chatbot to answer questions Based on its Contents. With this chatbot, You no longer have to Read through pages of text. Just ask your chatbot!

Building a Chatbot on Azure OpenAI using Net8

To get started, you will need to Create an Azure OpenAI resource in the Azure portal. We will guide you through the process of creating the resource and obtaining the required API keys and endpoints. Additionally, we will discuss the differences between Azure OpenAI and OpenAI, and how Azure OpenAI combines the advanced AI capabilities of OpenAI with the robust security and enterprise features of Microsoft Azure.

Creating an Azure OpenAI Resource and API

To build our chatbot, we will need to create an Azure OpenAI resource. We will walk through the necessary steps to create the resource and obtain the required API keys and endpoints. If you are a first-time customer, you may need approval from Azure.

Differences Between Azure OpenAI and OpenAI

In this section, we will explore the key differences between Azure OpenAI and OpenAI. Azure OpenAI offers private networking, regional availability, and responsible AI content filtering, among other enterprise features. By co-developing APIs with OpenAI, Azure OpenAI ensures compatibility and a smooth transition between the two platforms.

Setting Up the Development Environment

Before we start coding, let's set up our development environment. We will be using Visual Studio Code and a Mac for this demonstration. However, the code provided can also be used on Windows. We will create an MVC project named NetCon and ensure that our environment variables are properly loaded.

Creating a Chat Controller

In this section, we will create a chat controller to handle the chat functionality of our chatbot. We will use Visual Studio Code to create the controller and modify it to meet our requirements. We will also discuss the importance of privacy and how to handle user messages.

Creating an MVC Project with Visual Studio Code

To begin building our chatbot, we will create an MVC project using Visual Studio Code. We will walk you through the steps to create the project and set up the necessary files, including the .env file, packages.txt, and script.rtf. These files will help us configure our chatbot and manage its dependencies.

Working with Environment Variables and Packages

In this section, we will dive deeper into working with environment variables and packages. We will explain the purpose of the .env file and its contents, as well as the packages.txt file and script.rtf. We will also discuss the importance of managing dependencies and how to ensure we have all the required packages for our project.

Implementing Chat Functionality

Now it's time to implement the chat functionality of our chatbot. We will walk you through the code that enables the chatbot to communicate with the Azure OpenAI model. We will explain the role of chat completions and how to handle user messages. We will also cover the concept of tokens and the token limit.

Extracting Text from a PDF

To enable our chatbot to answer questions based on the contents of a PDF file, we need to extract the text from the PDF. We will discuss different approaches to extract text from PDF files and provide you with the necessary code to extract text using the iText library. We will also touch upon the importance of token limits and their impact on chatbot performance.

Integrating the PDF Data into the Chatbot

In this section, we will integrate the extracted text from the PDF into our chatbot. We will modify the chat functionality to include the PDF data and enable the chatbot to answer questions based on the PDF contents. We will showcase the chatbot's ability to provide accurate answers using the PDF data.

Limitations and Improvements

While our chatbot is functional, it has certain limitations that need to be addressed. We will discuss these limitations, such as token limits and training data, and provide suggestions for improvements. We will also explore the retrieval-augmented generation (RAG) pattern and how it can enhance the chatbot's accuracy and relevance.

Conclusion

In conclusion, building a chatbot on Azure OpenAI using Net8 allows for the creation of an intelligent and efficient conversational agent. By integrating PDF data, the chatbot becomes even more capable of providing accurate answers based on the contents of the PDF. However, there are limitations to consider, and improvements can be made to enhance the chatbot's performance. Overall, this technology offers exciting possibilities in the realm of AI-powered chatbots.

Highlights

• Building a chatbot on Azure OpenAI using Net8
• Integrating PDF data to enable contextual answers
• Leveraging AI capabilities of Azure OpenAI
• Retrieval-augmented generation (RAG) pattern for enhanced accuracy and relevance

FAQs

Q: Can I use a different development environment instead of Visual Studio Code? A: Yes, you can use any development environment of your choice as long as it supports working with ASP.NET MVC projects.

Q: How long does it take to create an Azure OpenAI resource? A: The creation process usually takes around 5 minutes. However, it may vary depending on the current workload on the Azure platform.

Q: Can I use openai instead of Azure OpenAI for building the chatbot? A: Yes, you can use openai instead of Azure OpenAI, but the implementation may differ slightly. You will need to follow the documentation and instructions provided by the openai platform.

Q: What are the token limitations in AI assistance? A: Token limitations refer to the maximum number of tokens that can be processed by the AI model. Exceeding the token limit may result in truncated answers or decreased performance.

Q: How can I improve the accuracy and relevance of the chatbot's answers? A: To improve accuracy and relevance, you can employ the retrieval-augmented generation (RAG) pattern and incorporate Azure search or other databases. This allows for enhanced information retrieval and better response quality.

Q: Can the chatbot handle multiple user messages and system Prompts? A: Yes, the chatbot can handle multiple user messages and system prompts. It can engage in complex conversations by processing and responding to multiple inputs.

Q: Are there any additional features or functionalities that can be added to the chatbot? A: Yes, there are various additional features and functionalities that can be added to the chatbot, depending on your specific requirements. These may include natural language processing, sentiment analysis, entity recognition, and more.