voyage-4-lite Embedding Model
Text embedding model optimized for general-purpose retrieval quality, latency, and cost for AI applications. 32K context length.
ExploreProduct 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-lite is a lightweight, general-purpose embedding model optimized for low latency and cost. Enabled by Matryoshka learning and quantization-aware training, voyage-4-lite supports embeddings in 2048, 1024, 512, and 256 dimensions, with multiple quantization options.
Learn more about voyage-4-lite here: https://blog.voyageai.com/2026/01/15/voyage-4
Highlights
Lightweight, general-purpose embedding model optimized for low latency and cost.
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.