Новая SOTA-модель эмбеддингов, которая обходит решения от OpenAI и Cohere по точности почти на 10-20%. ИИ идеально справляется с RAG-системами в таких сложных нишах, как юриспруденция, финансы и программирование, поддерживая мультиязычность.
TL;DR – Introducing voyage-3-large, a new state-of-the-art general-purpose and multilingual embedding model that ranks first across eight evaluated domains spanning 100 datasets, including law, finance, and code. It outperforms OpenAI-v3-large and Cohere-v3-English by an average of 9.74% and 20.71%, respectively. Enabled by Matryoshka learning and quantization-aware training, voyage-3-large supports smaller dimensions and int8 and binary quantization that dramatically reduce vectorDB costs with minimal impact on retrieval quality.