
Unlocking Creativity with Azure OpenAI
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In the previous chapter, we delved into advanced topics that expanded our understanding of language models. We became familiar with concepts such as embedding, which involves representing words or phrases in a numerical form for language model processing and storing the embedding into Azure Cognitive Search for use in relevance searches. Additionally, we explored the Model Context Window, which determines the amount of context a language model considers for generating predictions. We also discovered features such as Azure OpenAI On Your Data, which allows customer chat, model fine-tuning, and OpenAI function calling, enabling the execution of specific functions within the language model. The chapter further introduced OpenAI plugins, offering extensibility options for enhancing the functionality of language models. Finally, we were introduced to LangChain and Semantic Kernel, an application development framework for large language...