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voyage-multilingual-2 Embedding Model

Text embedding model optimized for multilingual retrieval and AI applications. 32K context length.

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About

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-multilingual-2 is optimized for multilingual retrieval and RAG, demonstrating superior multilingual retrieval quality and outperforming alternatives such as OpenAI v3 large and Cohere multilingual v3 on most languages, including French, German, Japanese, Spanish, and Korean. On average, it outperforms the second-best model by 5.6% and notably, continues to perform well on English. voyage-multilingual-2 supports a 32K context length. Latency is 90 ms for a single query with at most 100 tokens, and throughput is 12.6M tokens per hour at $0.22 per 1M tokens on an ml.g6.xlarge. Learn more about voyage-multilingual-2 here: https://blog.voyageai.com/2024/06/10/voyage-multilingual-2-multilingual-embedding-model/

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