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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

To construct this system, we need the following essential services:

  • Azure Cognitive Search
  • Azure OpenAI Service

Our primary objective is to transform unstructured data into embeddings, which will be stored within a vector database. When a user submits a query, the system leverages Azure OpenAI embeddings for processing. Subsequently, a vector search operation is performed on the vector database to retrieve the top K paragraphs. These selected paragraphs are then sent to the Azure OpenAI answering prompt, which extracts the answers and delivers them to the user.

The following is a simple architecture diagram for a question-and-answer solution using Azure OpenAI:

Figure 4.1: Architecture design

Figure 4.1: Architecture design

In the preceding diagram, we are sending Azure OpenAI Service embeddings to a vector database, and the questions asked by the user are sent to those embeddings and the results will be extracted. Now, wewill develop a solution...

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