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, fin…| Voyage AI
TL;DR – Introducing voyage-code-3, our next-generation embedding model optimized for code retrieval. It outperforms OpenAI-v3-large and CodeSage-large by an average of 13.80% and 16.81% on a suite …| Voyage AI
TL;DR – We are thrilled to launch our finance domain-specific embedding model voyage-finance-2, which demonstrates superior finance retrieval quality and outperformed competing models on fina…| Voyage AI
Model Choices Voyage currently provides the following text embedding models: Model Context Length (tokens) Embedding Dimension Description voyage-3-large 32,000 1024 (default), 256, 512, 2048 The best general-purpose and multilingual retrieval quality. See blog post for details. voyage-3 32,000 1024...| Voyage AI
TL;DR – Domain-specific and custom embedding models have been shown to enhance the retrieval quality significantly. Hot on the heels of the state-of-the-art code embedding model (voyage-code-2), we…| Voyage AI
TL;DR – Voyage is a team of leading AI researchers, dedicated to enabling teams to build better RAG applications. Today, we’re releasing a new state-of-the-art embedding model and API, which alread…| Voyage AI