Unlock the power of Azure OpenAI Service with your own data

Table of Contents

1. Introduction
2. Uploading Data Files
3. Creating the Open AI Service
4. Specifying the Data Source
5. Configuring the Search Type
6. Testing the Solution
7. Calling the Service from a Python Application
8. Conclusion

Introduction

In this article, we will explore how to Create a Generative AI solution that incorporates custom data to build a chatbot that responds with knowledge from our own documents. We will use Azure services such as Azure open AI service, cognitive search service, and storage account to implement this solution.

Uploading Data Files

Before we can start using our custom data, we need to upload the necessary files. These files can include emergency airworthiness directives from the FAA, operating manuals for aircraft, and any other Relevant documents. We will upload these files to a storage account so that we can combine them with the base model for our AI searches.

Creating the Open AI Service

To create our Generative AI Solution, we will use the Azure open AI service. We will go through the process of creating a deployment from scratch using the GPT-3.5-turbo 16k model. We will give our deployment a name and specify the data source.

Specifying the Data Source

In order for our chatbot to access our custom data, we need to specify the data source. We will choose the blob storage service where we uploaded our files, select the container, and choose the cognitive search resource to use. We will also give the index a name for referencing purposes.

Configuring the Search Type

For our generative AI solution, we will start with a basic keyword search. This means that our chatbot will be able to search for specific keywords within our custom data. We can later enhance the search capabilities if needed.

Testing the Solution

Once we have set up our generative AI solution, we can test its functionality. We can type in a query and see how our chatbot responds with knowledge from our custom data. We can also experiment with formatting options to make the responses more concise and easier to understand.

Calling the Service from a Python Application

To integrate our generative AI solution into a Python application, we can use the Azure open AI SDK. We need to provide the necessary information such as the deployment ID, API key, cognitive search index name, and key. By making the appropriate calls to the open AI service, we can retrieve responses from our chatbot.

Conclusion

In this article, we have learned how to create a generative AI solution that incorporates custom data using Azure services. We have explored the process of uploading data files, creating the open AI service, specifying the data source, configuring the search type, testing the solution, and integrating it into a Python application. By harnessing the power of AI, we can enhance our chatbot's capabilities and provide more personalized responses to users.

Highlights

• Creating a generative AI solution with custom data
• Uploading and combining data files with the base model
• Configuring the search type for the chatbot
• Testing the chatbot's functionality
• Integrating the solution into a Python application

FAQs

Q: Can I upload any type of document as my custom data? A: Yes, you can upload a variety of documents such as PDFs, manuals, and directives.

Q: Can I use a different AI model instead of GPT-3.5-turbo 16k? A: Yes, you can experiment with different AI models to find the one that best suits your needs.

Q: How can I format the responses from the chatbot? A: You can experiment with different formatting options such as bullet lists or numbered lists to make the responses more readable.

Q: Can I use this generative AI solution for other applications? A: Yes, you can adapt this solution for various applications that require personalized responses based on custom data.

Q: What are the benefits of using generative AI for chatbots? A: Generative AI allows chatbots to provide more accurate and relevant responses by incorporating custom data and generating contextually appropriate answers.

Q: Does this solution incur any additional charges? A: Yes, using the cognitive search service may incur charges, so it's important to consider this when implementing the solution.