# Embeddings

> Dense numeric vectors that represent text (or other data) so that semantically similar items sit close together in vector space.

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- **term:** Embeddings
- **category:** knowledge-memory
- **short_def:** Dense numeric vectors that represent text (or other data) so that semantically similar items sit close together in vector space.
- **long_def:** The modern approach was crystallized by Word2vec (Mikolov, Chen, Corrado and Dean at Google, 2013), which learned high-quality dense word vectors that captured meaning by context. Today, embedding models turn documents and queries into vectors so similarity search can find relevant content by meaning rather than keyword — the retrieval engine under RAG and vector databases.
- **see_also:** vector-database, rag, agentic-rag
- **etymology_origin:** The dense-vector approach was established by Word2vec, introduced by Tomáš Mikolov, Kai Chen, Greg Corrado and Jeffrey Dean at Google in 2013 (arXiv 1301.3781); the broader 'word embedding' concept predates it.
- **related_to:** vector-database, rag, agentic-rag, grounding
- **contrast_with:** Unlike keyword indexing, which matches exact tokens, embeddings place text in a continuous vector space so 'meaning-similar' content is found by vector distance — semantic match rather than lexical match.
- **example:** Word2vec (Mikolov et al., Google, 2013) showed dense word vectors capture semantics so well that vector arithmetic like 'king − man + woman ≈ queen' holds.
- **source:** https://en.wikipedia.org/wiki/Word_embedding
- **status:** active
- **why_it_matters:** Embeddings are how agents retrieve your content by meaning; clean, well-structured text embeds and retrieves better, making your site easier to surface in RAG answers.
- **sameAs:** https://en.wikipedia.org/wiki/Word_embedding, https://en.wikipedia.org/wiki/Word2vec
- **bridge_entity:** models
- **last_verified:** 2026-06-15
- **md_twin:** /glossary/embeddings.md