What are Vector Embeddings?
Vector Embeddings are a core building block of modern Artificial Intelligence and Large Language Models (LLMs). They are the mechanism used to translate unstructured data, such as a paragraph of text, an audio file, or an image, into a dense array of numbers (a Vector) that a computer can process and compare.
A machine learning model cannot read English directly; it works on numbers. Historically, engineers assigned arbitrary IDs to words (e.g., Apple = 1, Banana = 2), but those numbers carried no meaning. Vector Embeddings solve this by placing words, sentences, or documents into a high-dimensional coordinate system (often hundreds to a few thousand dimensions), where distance between vectors reflects how related the concepts are.
The Architecture of Semantic Space
When text is run through an Embedding Model (like OpenAI’s text-embedding-3-small), the model outputs an array of floating-point numbers.
For example, the word “King” might be translated into [0.45, -0.12, 0.89, ...], with 1,536 numbers for that model.
The key property of embeddings is the spatial relationship.
If you map the vector for “King”, the vector for “Man”, and the vector for “Woman”, you can do arithmetic with concepts. In the word2vec word embeddings published by Google researchers in 2013, taking the vector for [King], subtracting [Man], and adding [Woman] gives a point whose nearest word vector is [Queen]. The model does not “know” what a Queen is; it has learned that the relationship between King and Queen resembles the one between Man and Woman. Modern sentence embedding models capture similar relationships, though not always this cleanly.
Vector Embeddings in the Data Lakehouse
Embeddings are the core engine powering the Retrieval-Augmented Generation (RAG) architecture.
When an enterprise wants to build a custom AI chatbot that understands their proprietary HR manuals, they cannot fit 10,000 PDF files into the LLM’s context window.
- Data engineers extract the text from the PDFs.
- They run the text through an Embedding Model, translating millions of sentences into Vector Embeddings.
- They store these numerical arrays in a Vector Database (or an Open Data Lakehouse engine that supports vector indexing).
When an employee asks the chatbot, “What is the maternity leave policy?”, the system translates that specific question into its own Vector Embedding. The database then runs a fast similarity search (using a measure such as Cosine Similarity), finding the HR paragraphs whose vectors are closest to the question’s vector. It returns those specific paragraphs to the LLM to generate the final answer.
Summary of Technical Value
Vector Embeddings are the translation layer between human language and the numbers computers work with. By mapping meaning and context into dense numerical coordinates, embeddings let organizations search large collections of unstructured text, audio, and images by meaning, and they are a core component of modern generative AI applications.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.