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Unlocking Creativity with Azure OpenAI

Unlocking Creativity with Azure OpenAI

By : AMIT MUKHERJEE, Adithya Saladi
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Unlocking Creativity with Azure OpenAI

Unlocking Creativity with Azure OpenAI

By: AMIT MUKHERJEE, Adithya Saladi

Overview of this book

Azure OpenAI, a cutting-edge service by Microsoft, harnesses the power of OpenAI's Large Language Model (LLM) to drive cloud-based innovations within enterprises. This service integrates advanced LLM models into business functions, transforming Microsoft products like GitHub Copilot, Microsoft 365 Copilot, and Bing Chat, making them more advanced and interactive. Azure OpenAI is accessible via REST APIs, Python SDK, or Azure OpenAI Studio, opening doors to build innovative AI applications. This book is a comprehensive guide to build GenAI applications using Azure OpenAI. It begins with the fundamentals, including how to access Azure OpenAI and how to effectively utilize its REST API and Python SDK. It takes a deep dive into various AI models and emphasizes the crucial aspects of prompt engineering and fine-tuning for optimal output. It further underlines the significance of content filters and prevention of misuse, maintaining a strong focus on safety and security protocols and finally the significance of Azure OpenAI Studio in deployment and administration is emphasized. Practical applications are showcased like content generations, summarization, semantic search, code documentation and code generation. Combining Azure Cognitive services amplifies Generative AI potential and finally aligns with Microsoft's ethical AI principles.
Table of Contents (19 chapters)
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Free Chapter
1
Part 1: Foundations of Generative AI and Azure OpenAI
5
Part 2: Practical Applications of Azure OpenAI: Real-World Use Cases
13
Part 3: Mastering Governance, Operations, and AI Optimization with Azure OpenAI

Summary

In this chapter, we installed the necessary tools on our local machine, including Python, the Azure Developer CLI, Node.js, Git, and PowerShell. We also ensured we had an Azure account and subscription with access to the Azure OpenAI service. Additionally, we made sure to have a basic understanding of Azure services and Python.

Next, we focused on building the solution using Azure Cognitive Search and Azure OpenAI Service. We worked within a Jupyter notebook to develop the code. We started by importing the required packages, such as openai, os, and dotenv.load_dotenv. Then, we loaded the .env file and extracted the necessary variables, such as OPENAI_API_KEY, OPENAI_DEPLOYMENT_ENDPOINT, and others. We initialized Azure OpenAI with these variables.

After setting up the environment, we generated a function to add names to a database based on a provided prompt. Following that, we created a SQL query to retrieve all records from the database we had just created. Finally,...

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