From the course: Building a Project with the ChatGPT API

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Measure relatedness using embeddings

Measure relatedness using embeddings - ChatGPT Tutorial

From the course: Building a Project with the ChatGPT API

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Measure relatedness using embeddings

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- Have you ever wondered how advanced searching works? What about detecting emotions and product reviews or extracting underlying topics or themes from a collection of text documents? Well, today you are going to find out. The Embeddings API measures similarities of text strings by mapping text and even code to a vector, also known as a list representation. This transforms text into a sequence of floating point numbers. This numeric representation of text enables machine learning models and algorithms to comprehend the connections and associations among concepts more easily. The distance between two vectors measures their relatedness. For example, if two pieces of text are similar, their vector representations should also be similar. Think of embeddings like a map. Just as a map represents the spatial relationships between different locations, a word embeddings model represents the semantic relationships between words.…

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