Intermediate · Fundamentals

Vector Embeddings

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. Numerical representations of text, images, or other data that capture semantic meaning, allowing for mathematical operations like similarity comparisons.

Technical Definition

Numerical representations of text, images, or other data that capture semantic meaning, allowing for mathematical operations like similarity comparisons.

How it works

Vector embeddings are dense, low-dimensional representations where items with similar meanings are located closer together in a multi-dimensional space. These embeddings are generated by models trained to capture context and relationships. They are fundamental for tasks such as semantic search, recommendation systems, and RAG, as they enable computers to understand and compare complex data types.

Related Concepts

• Retrieval-Augmented Generation (RAG) — A technique that enhances LLM responses by retrieving relevant documents from an external knowledge base before generating an answer.
• Vector Database — A specialized database optimized for storing, indexing, and querying high-dimensional embedding vectors using similarity search.
• Dimensionality reduction — The process of reducing the number of features or variables in a dataset while retaining essential information.
• Semantic Search — A search technology that understands the intent and contextual meaning of queries, rather than just matching keywords.