voyage-4 Embedding Model
Text embedding model optimized for general-purpose (including multilingual) retrieval/search and AI applications. 32K context length.
Product Description
Overview
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-4 is a general-purpose (including multilingual) embedding model optimized for retrieval/search and AI applications. voyage-4 supports embeddings in 2048, 1024, 512, and 256 dimensions, with multiple quantization options.
Learn more about voyage-4 here: https://blog.voyageai.com/2026/01/15/voyage-4
Highlights
General-purpose (including multilingual) embedding model optimized for retrieval/search and AI applications.
Supports embeddings of 2048, 1024, 512, and 256 dimensions and offers multiple embedding quantization, including float (32-bit floating point), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8).
32K token context length.
