This article discusses text-based embeddings, including traditional word embeddings using Word2Vec, contextualized word embeddings using BERT, and sentence embeddings using sentence transformer models. It also covers large language models (LLMs) such as Falcon and Mistral, which use text-embeddings based on the transformer architecture. The article explains how to use these techniques in practice with Python code examples and highlights their limitations and use cases.