Embedding Model
An embedding model is a machine-learning model that maps text (or images, audio, code) to a fixed-dimensional vector such that semantically similar inputs land near each other in vector space. It is distinct from the language model that generates responses — in a RAG pipeline the embedding model is used for indexing and retrieval, and a separate generator model produces the final answer. Choice of embedding model materially affects retrieval quality; common 2026 options include OpenAI text-embedding-3, Cohere embed-v3, Voyage's embedding family, and open-source models like BGE and E5. Dimensionality (typical choices: 768, 1024, 1536, 3072) trades off storage and query cost against representational capacity, and newer models often support Matryoshka embeddings where a shorter prefix of the vector is still useful.
Example
A startup benchmarks three embedding models on its own 500-question eval set before shipping its RAG product: a small open-source model, a mid-tier Voyage model, and OpenAI text-embedding-3-large. The open-source model is free but scores 0.61 on top-5 recall; Voyage scores 0.74; OpenAI scores 0.78. The team picks Voyage — the recall gap to OpenAI does not justify the per-document cost at their ingest volume, and the gap over the open-source model is large enough to matter to end users.
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