The world of AI has seen significant advancements, particularly in the realm of Large Language Models (LLMs). These models enable various tasks, such as semantic search and text classification, with enhanced accuracy and efficiency. Vector embeddings are a crucial aspect of LLMs, capturing the essence of data in a continuous vector space. They preserve semantic relationships between objects, making it possible to perform complex computations more efficiently. Various techniques exist for generating these embeddings, including Word2Vec, GloVe, BERT, and transformer models. These tools can be used to create text embeddings using platforms like SingleStore Notebooks and embedding models from OpenAI, Cohere, and HuggingFace. Once created, vector embeddings can be stored in databases, enabling applications such as indexed approximate-nearest-neighbor search. The exploration of embeddings continues to unveil innovative ways to capture data complexities, promising broader application and effectiveness in machine learning and data science.