Bring your own data to Azure OpenAI chat models

Introduction

Azure OpenAI models provide a secure and robust solution for tasks like creating content, summarizing information, and various other applications that involve working with human language. Now you can operate these models in the context of your own data. Try Azure OpenAI Studio today to naturally interact with your data and publish it as an app from from within the studio.

Getting Started

Follow this quickstart tutorial for pre-requisites and setting up your Azure OpenAI environment.

In order to try the capabilities of the Azure OpenAI model on private data, I am uploading an ebook to the Azure OpenAI chat model. This e-book is about "Serverless Apps: Architecture, patterns and Azure Implementation" written by Jeremy Likness and Cecil Phillip. You can download the e-book here

Before uploading own data

Prior to uploading this particular e-book, the model's response to the question on serverless design patterns is depicted below. While this response is relevant, let's examine if the model is able to pick up the e-book related content during the next iteration

After uploading own data

This e-book has an exclusive section that talks in detail about different design patterns like Scheduling, CQRS, Event based processing etc.

After training the model on this PDF data, I asked a few questions and the following responses were nearly accurate. I also limited the model to only supply the information from the uploaded content. Here's what I found.

Now when I asked about the contributors to this e-book, it listed everyone right.

Read more

With enterprise data ranging to large volumes in size, it is not practical to supply them in the context of a prompt to these models. Therefore, the setup leverages Azure services to create a repository of your knowledge base and utilize Azure OpenAI models to interact naturally with them.

The Azure OpenAI Service on your own data uses Azure Cognitive Search service in the background to rank and index your custom data and utilizes a storage account to host your content (.txt, .md, .html, .pdf, .docx, .pptx). Your data source is used to help ground the model with specific data. You can select an existing Azure Cognitive Search index, Azure Storage container, or upload local files as the source we will build the grounding data from. Your data is stored securely in your Azure subscription.

We also have another Enterprise GPT demo that allows you to piece all the azure building blocks yourself. An in-depth blog written by Pablo Castro chalks the detail steps here.

Getting started directly from Azure OpenAI studio allows you to iterate on your ideas quickly. At the time of writing this blog, the completions playground allow 23 different use cases that take advantage of different models under Azure OpenAI.

1. Summarize issue resolution from conversation
2. Summarize key points from financial report (extractive )
3. Summarize an article (abstractive)
4. Generate product name ideas
5. Generate an email
6. Generate a product description (bullet points)
7. Generate a listicle-style blog
8. Generate a job description
9. Generate a quiz
10. Classify Text
11. Classify and detect intent
12. Cluster into undefined categories
13. Analyze sentiment with aspects
14. Extract entities from text
15. Parse unstructured data
16. Translate text
17. Natural Language to SQL
18. Natural language to Python
19. Explain a SQL query
20. Question answering
21. Generate insights
22. Chain of thought reasoning
23. Chatbot

Resources

There are different resources to get you started on Azure OpenAI. Here's a few:

• Apply for access to Azure OpenAI
• Azure OpenAI Service - Documentation, quickstarts, API reference - Azure Cognitive Services | Microsoft Learn
• Introduction to Azure OpenAI Service - Training | Microsoft Learn
• GitHub - Azure/azure-openai-samples
• Revolutionize your Enterprise Data with ChatGPT