Update vector-embeddings

Author: wmwxwa

articles/cosmos-db/gen-ai/vector-embeddings

@@ -10,7 +10,9 @@ ms.date: 07/01/2024
 
 # What are vector embeddings?
 
-Vectors, also known as embeddings or vector embeddings, are mathematical representations of data in a high-dimensional space. They represent various types of information — text, images, audio — a format that machine learning models can process. Each embedding is a vector of floating-point numbers, such that the distance between two embeddings in the vector space is correlated with semantic similarity between two inputs in the original format. For example, if two texts are similar, then their vector representations should also be similar. These high-dimensional representations capture semantic meaning, making it easier to perform tasks like searching, clustering, and classifying.
+Vectors, also known as embeddings or vector embeddings, are mathematical representations of data in a high-dimensional space. They represent various types of information — text, images, audio — a format that machine learning models can process. When an AI model receives text input, it first tokenizes the text into tokens. Each token is then converted into its corresponding embedding. The model processes these embeddings through multiple layers, capturing complex patterns and relationships within the text. The output embeddings can then be converted back into tokens if needed, generating readable text.
+
+Each embedding is a vector of floating-point numbers, such that the distance between two embeddings in the vector space is correlated with semantic similarity between two inputs in the original format. For example, if two texts are similar, then their vector representations should also be similar. These high-dimensional representations capture semantic meaning, making it easier to perform tasks like searching, clustering, and classifying.
 
 Here are two examples of texts represented as vectors: