Embeddings

Numeric vectors produced by a model so items can be compared or used by downstream machine-learning and retrieval systems.

An embedding model maps text, images, audio, code, or other inputs into a vector space learned from data and objectives. Nearby vectors often share task-relevant patterns, so a password-reset question may sit near a differently worded login-recovery question. What proximity represents depends on the model, training, input format, and distance function; an embedding is not a complete stored meaning of the item.

Builder example

Embeddings support semantic retrieval, clustering, recommendations, deduplication, and classification features. Performance can shift across domains, languages, document lengths, and query styles. Choose with a labeled evaluation from the actual workload rather than from model descriptions alone.

A user searches for how to cancel their account. The embedding puts 'cancel my account' near 'set up my account' because both are about account actions.

Test embeddings with your real queries and edge cases. Combine embedding search with keyword filters to catch these near-misses.

Common confusion: Embeddings measure pattern similarity, not factual correctness. Two sentences can be "close" in embedding space while saying opposite things, because the model sees them as topically related.