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voyage-law-2 Embedding Model

Text embedding model optimized for legal retrieval and AI applications. Tops the MTEB leaderboard for legal retrieval. 16K 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-law-2 is an embedding model optimized for retrieving legal texts. It excels in AI applications such as semantic case retrieval, legal question answering, and legal AI assistants. On 8 legal retrieval tasks, voyage-law-2 shows a 5.62% improvement over alternatives, including OpenAI v3 large, Cohere English v3, and E5 Mistral. It also enhances general-purpose corpora and long-context retrieval tasks, exceeding OpenAI v3 large by over 15% on average. 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-law-2 here: https://blog.voyageai.com/2024/04/15/domain-specific-embeddings-and-retrieval-legal-edition-voyage-law-2/

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