Vector Embeddings

Vector embeddings are dense numerical representations of data (text, images, audio) in high-dimensional space. They capture semantic meaning, so items with similar meanings have similar vector representations.

For text, an embedding model converts a sentence like "How do I deploy to production?" into a vector of 768-3072 floating-point numbers. Similar questions like "What's the deployment process?" would produce vectors that are close in this space, even though they use different words.

Embeddings are fundamental to modern AI applications. They power semantic search (finding relevant results by meaning, not keywords), recommendation systems, clustering, anomaly detection, and RAG pipelines.

Popular embedding models include OpenAI's text-embedding-3, Cohere's embed-v3, and open-source options like sentence-transformers. The choice of embedding model affects the quality of downstream tasks and the dimensionality of the vectors stored.