In Depth
Embedding models are trained specifically to produce high-quality vector representations where semantically similar inputs map to nearby points in the embedding space. Unlike general-purpose language models that generate text, embedding models output fixed-dimensional numerical vectors (typically 384 to 4096 dimensions) optimized for measuring similarity and enabling retrieval.
Leading embedding models include OpenAI's text-embedding-3, Cohere's embed models, Google's Gecko, and open-source options like E5, BGE, and GTE. These are typically trained using contrastive learning on large datasets of paired texts (questions and answers, queries and relevant documents). The quality of embeddings is measured on retrieval benchmarks like MTEB (Massive Text Embedding Benchmark).
Embedding models are foundational infrastructure for RAG systems, semantic search, clustering, classification, and anomaly detection. They enable applications to work with meaning rather than keywords. For businesses building AI applications, choosing the right embedding model involves trade-offs between dimensionality (affecting storage and search speed), quality (retrieval accuracy), multilingual support, and cost. Most modern AI applications that work with text rely on embedding models as a core component.