What is Embedding Model?
TL;DR
A specialized model that converts text or images into numerical vectors that preserve semantic meaning. The foundation for search and RAG.
Embedding Model: Definition & Explanation
An Embedding Model is a specialized model that converts data such as text, images, and audio into high-dimensional numerical vectors (embedding vectors) that preserve semantic information. Numerous models exist, including OpenAI text-embedding-3-large, Google Gecko, Cohere Embed, BGE, and E5. Since semantically similar sentences are represented as nearby vectors, similarity can be computed using measures like cosine similarity. RAG retrieval accuracy, semantic search quality, and recommendation relevance all depend heavily on the quality of the embedding model used. Model performance is compared on the MTEB (Massive Text Embedding Benchmark).