# Embedding

> An embedding is a vector of numbers representing text's meaning, placed so similar texts land close together — the foundation of semantic search and RAG.

**An embedding is a numeric vector representing a piece of text (or image, or code) in a high-dimensional space arranged by meaning — texts that say similar things get vectors that sit close together.**

An *embedding model* does the mapping: text in, a few hundred to a few thousand floating-point numbers out. Distance in that space approximates semantic similarity, which turns "find documents about X" into geometry: embed the query, find the nearest stored vectors. That single trick underlies [semantic search](/glossary/semantic-search), [RAG](/glossary/rag) retrieval, recommendation, clustering, and deduplication.

Two practical truths dominate embedding work. First, **the model choice is load-bearing and sticky** — quality varies by domain and language, and switching models later means re-embedding everything; the trade-offs across OpenAI, Cohere, Voyage, and open-source options are mapped in [Choosing Embeddings in 2026](/guides/concepts/choosing-embeddings-2026). Second, **embeddings are stored and searched in a [vector database](/glossary/vector-database)**, whose indexing choices set your speed/recall trade-off. When retrieval misbehaves, diagnose the embedding set before blaming the retriever — that's exactly what the [embedding-set-inspector](/skills/data/embedding-set-inspector) skill does.

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_Source: https://agentscamp.com/glossary/embedding — Term on AgentsCamp._