What are Embedding Models? - Definition & Meaning
Learn what embedding models are, how text is converted to vectors for semantic search and RAG, and which models to choose for your use case.
Definition
Embedding models are AI models that convert text (or other data) into numerical vectors of fixed dimension. Similar texts get similar vectors, enabling semantic search and similarity search.
Technical explanation
Embeddings capture semantic meaning; cosine similarity or dot product measures "distance" between texts. Models: OpenAI text-embedding-3 (3072 dim), Cohere embed, Voyage AI, open source (sentence-transformers, E5, BGE). Dimensions: 384–3072. Multilingual models support multiple languages. Trade-offs: quality vs. cost, dimension vs. retrieval speed, multilingual vs. language-specific.
How AVARC Solutions applies this
AVARC Solutions uses embedding models for all RAG and search projects. We choose based on language (Dutch/multilingual), cost, and latency. For Dutch content we prefer multilingual models or Dutch-finetuned variants.
Practical examples
- A RAG system with text-embedding-3-small for fast, cost-efficient retrieval.
- A multilingual knowledge base with a multilingual embedding model for NL/EN queries.
- A semantic search using BGE or E5 for state-of-the-art retrieval quality.
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