Writing fiction stories with large language models (LLMs) can be challenging, especially when the story spans multiple chapters or scenes. LLMs have a hard time processing large amounts of text because they have a limited context window. This means that they can only remember a certain number of tokens or words at a time. This causes them to ‘forget’ information outside of their context window, such as the names, traits, and actions of the characters. This is problematic if you are using

(A series of embedding vectors plotted by http://projector.tensorflow.org/ ) An embedding is a way to represent a piece of text that captures what it means. It is represented as a vector of floating-point numbers where each element has a specific value and position within the vector. The relationships between these values help to represent the meaning of the original text in a way that machine learning models can understand. If two embeddings are close together, it means that th

This article will demonstrate how Azure OpenAI Function calling can be used with the Retrieval Augmented Generation (RAG) pattern. The advantages of using this method over the method described in the article: Azure OpenAI RAG Pattern using a SQL Vector Database are: This method allows the model to make multiple vector searches if needed The developer is not required to create a complex  prompt of instructions   Rag Pattern VS Functions As described in the

The Azure OpenAI API is a powerful tool that allows developers to leverage the natural language understanding and generation capabilities of GPT models. However, sometimes developers may want to use the model’s output to interact with other services or applications, such as sending an email, booking a flight, or creating a chart. How can they do that without having to parse the model’s output manually or write complex logic to handle different scenarios? The answer is function calling . Fu