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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

Architecture design

We will be using a multi-agent system where the User Proxy Assistant acts as the main orchestrator, facilitating communication between the user and specialized agents. The DALL-E Assistant is responsible for generating images based on user queries, while the Vision Assistant analyzes these images using the GPT-4o model. The system maintains ongoing communication through persistent threads and utilizes function calls to perform specific tasks, such as image generation and analysis.

In a typical workflow, the user sends a query to the User Proxy Assistant, which forwards it to the DALL-E Assistant to create an image. This image is then analyzed by the Vision Assistant, which provides feedback that can be used to refine the image further. This coordinated effort between specialized agents ensures efficient handling of complex tasks.

Important note

This is just one way to implement multi-agent systems. You could also use other frameworks such as AutoGen, Semantic...

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