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voyage-3 Embedding Model

Text embedding model optimized for general-purpose (including multilingual) retrieval/search 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-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). Latency is 75 ms for a single query with at most 200 tokens, and throughput is 57M tokens per hour at $0.06 per 1M tokens on an ml.g6.xlarge. Learn more about voyage-3 here: https://blog.voyageai.com/2024/09/18/voyage-3/

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