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-law-2 is a cutting-edge embedding model that is optimized for semantic retrieval of legal texts. The model excels in law-related AI applications, including semantic case retrieval, legal question answering, and various functions of general legal AI assistants. On 8 legal retrieval tasks, voyage-law-2 has a significant 5.62% improvement over any alternatives, including OpenAI v3 large, Cohere English v3 and E5 Mistral. voyage-law-2 also has consistent enhancements across general-purpose corpora and long-context retrieval tasks, exceeding OpenAI v3 large on average by over 15%. Learn more about voyage-law-2 here.