Embeddings

Embeddings convert words, sentences, or entire documents into vectors — lists of numbers in a high-dimensional space. The key insight is that semantically similar concepts end up close together in this space. "Dog" and "puppy" have similar embeddings; "dog" and "quantum physics" do not.

This mathematical representation of meaning enables powerful applications. Semantic search finds documents by meaning, not just keyword matching. Recommendation systems suggest similar items. Clustering algorithms group related content automatically. And RAG systems use embeddings to retrieve the most relevant context for any query.

Embedding models are separate from generation models. While an LLM like Claude generates text, an embedding model like OpenAI's text-embedding-3 or Cohere's embed converts text to vectors. These vectors are stored in vector databases for fast similarity search. At Agentik {OS}, embeddings power our knowledge retrieval systems, ensuring every agent has access to the right information at the right time.