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AI2026-05-17

Embedding

Also known as: Embedding / 埋め込み表現 / ベクトル埋め込み

The transformation of text or other data into high-dimensional vectors where semantic proximity is preserved — the core representation technique underlying RAG, semantic search, and recommendation systems.

Overview

Embedding converts words, sentences, documents, or images into numeric vectors where semantically similar items cluster together. Cosine similarity or dot-product distance then enables nearest-neighbor lookup. Popular models include OpenAI text-embedding-3, BGE, and E5.

Applications

RAG document retrieval, semantic search, duplicate detection, document clustering, and recommendation engines all depend on embeddings. Storing vectors in a purpose-built vector database makes sub-second similarity search feasible at scale.

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Related Terms

Vector Database Semantic Search RAG (Retrieval-Augmented Generation)Tokenization GraphRAG

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