Text embedding models are neural networks that transform texts into numerical vectors. They are a crucial building block for semantic search/retrieval systems and retrieval-augmented generation (RAG) and are responsible for the retrieval quality. voyage-3 is a general-purpose embedding that: [1] outperforms OpenAI v3 large across all eight evaluated domains (tech, code, web, law, finance, multilingual, conservation, and long-context) by 7.55% on average, [2] has a 3-4x smaller embedding dimension (1024) compared to OpenAI (3072) and E5 Mistral (4096), resulting in 3-4x lower vectorDB costs, and [3] supports a 32K-token context length, compared to OpenAI (8K) and Cohere (512). Learn more about voyage-3 here.