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-finance-2 is optimized for finance domain retrieval and RAG. It demonstrates superior finance retrieval quality and outperformed competing models on financial retrieval datasets, with an average of 7% gain over OpenAI and 12% over Cohere. voyage-finance-2 supports a 32K context length. Learn more about voyage-finance-2 here.