Building a Custom Chatbot with Azure OpenAI & Your Data

Introduction

Chatbots are becoming essential tools for businesses, helping to improve customer service and automate various tasks. While there are many ready-made chatbot solutions available, they often don't meet specific business needs. This is why creating a chatbot using your own data is important. It ensures the chatbot understands and responds to your customers in the best way possible.
Azure OpenAI offers a powerful platform for building such custom chatbots. It combines OpenAI's advanced language models with Microsoft Azure's strong infrastructure. This combination allows you to create smart chatbots that can understand and generate human-like text and tailor them to work with your specific data. This means your chatbot can give more accurate and relevant responses.
In this article, we will walk you through how to build a chatbot with Azure OpenAI using your own data. We will cover everything from setting up your Azure account to training the chatbot with your data and deploying it for use. Whether you want to improve customer service, automate support, or create new ways to engage users, this guide will help you create a chatbot that fits your needs perfectly.

Prerequisites

Before you begin building a chatbot with Azure OpenAI using your own data, there are a few prerequisites you'll need to meet:

Azure Subscription: You need an active Azure subscription to access Azure OpenAI services. If you don’t have one, you can sign up for a free account on the Azure website.
Basic Knowledge of Azure Services: Familiarity with Azure services, including Azure Cognitive Services and Azure Storage, will be helpful. Understanding how to navigate the Azure portal and manage resources is essential. Your own data should be available in blob storage
Programming Knowledge: Basic knowledge of programming, particularly in Python or another programming language that supports RESTful API calls, is necessary. You'll be writing scripts to interact with Azure OpenAI and process data.

Let's start Step by Step implementation

Step 1. Create Azure OpenAI service.

Open https://portal.azure.com/
Log in with your Azure account.
Click on Create Resource - Azure OpenAI
Azure OpenAI combines OpenAI's advanced language models with Microsoft Azure's robust infrastructure, enabling the creation of intelligent applications that understand and generate human-like text. It provides a powerful platform for developing custom solutions tailored to specific business needs.
Search for Azure OpenAI service and click on it.
Provide basic details.
Fill in the necessary information, including the subscription, resource group, and region. Then, specify the name and pricing tier for your Azure OpenAI resource.
Once all details are completed, click "Review + Create" to proceed with the resource creation.
Once the Resource is Created, Proceed to Step 2

Step 2. Create deployment for the gpt-35-turbo model.

Open created Azure opener service
Access the Azure OpenAI service you created by selecting "Go to Azure OpenAI Studio" from the overview tab on the left.
Afterward, you'll be redirected to Azure OpenAI Studio. Navigate to the Deployments section by clicking on it in the left menu.
Select "Create new deployment" to initiate the deployment process for the GPT-3.5 Turbo model.
A popup window will appear, prompting you to create a deployment. Choose the "get-35-turbo" model, known for its robustness and versatility. This model can be fine-tuned with your specific data and is ideal for tasks such as generating exam questions and various content creation needs.
After the deployment process is complete, navigate to the Chat feature by selecting it from the menu on the left-hand side.
Next, access the "Add your data" tab and click on "Add a data source" to proceed.
Open the "Add data" popup and enter the required details.
- Choose "Azure Blob Storage" from the first dropdown menu.
- Select your subscription from the available options.
- Pick the Azure Blob storage resource where your custom data is uploaded.
- Choose the container containing your data.
- If you don't have an Azure AI Search service, click here to create one.
- Provide a name for the index.
- Optionally, set the indexer schedule to "Once," "Daily," or "Hourly."
- Proceed by clicking "Next."
After configuring the data source, click on "Save and Close." Please note that it may take approximately 3-5 minutes to complete the indexing process. Once the indexing is finished, you can utilize the chat emulator to ask any questions for testing purposes.
I've uploaded my resume and posed a few questions about it. Check out the awesome responses.

I hope you've enjoyed your new learning experience.

Thanks!