Embeddings

Embeddings are dense numerical vectors that represent the meaning of text, images, audio, or other data in a high-dimensional space. They are the mathematical foundation of how AI understands similarity and relationships—when two pieces of content are semantically similar, their embedding vectors are close together.

Every modern language model works with embeddings internally, but the term most commonly refers to purpose-built embedding models that convert inputs into fixed-size vectors for downstream use. OpenAI's text-embedding-3, Voyage AI, Cohere Embed, and open-source models like BGE and E5 generate vectors (typically 768–3072 dimensions) that can be stored, compared, and searched at scale.

Embeddings power a critical layer of the AI stack. Vector search uses embeddings to find semantically similar content. Retrieval-Augmented Generation (RAG) uses them to ground LLM responses in relevant documents. Recommendation systems use them to match users with content. Classification, clustering, anomaly detection—all become possible once data is represented as embeddings.

The concept extends far beyond text. Multimodal embeddings place images, text, and audio in a shared vector space—enabling search across modalities (find images matching a text description, or text matching an audio clip). CLIP, SigLIP, and ImageBind demonstrated that aligning embeddings across modalities unlocks powerful cross-modal reasoning. For the agentic web, embeddings are the substrate that enables AI systems to understand, compare, and retrieve information from the growing ocean of digital content.