voyage context-4 AI Foundry

Contextualized chunk embedding models are novel neural networks that encode not only the chunk’s own content, but also capture the contextual information from the full document 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-context-4 is a next-generation contextualized chunk embedding model that delivers higher retrieval accuracy, more effective long-document support, and built-in auto-chunking, with full support for both overlapping and non-overlapping chunks. Users can submit a full document as a single string and let the backend chunk it automatically, with chunk text returned in the response for inspection and storage. voyage-context-4 delivers approximately 1.4 NDCG@10 points higher chunk-retrieval quality than voyage-context-3 and approximately 8.4 NDCG@10 points higher than cohere-embed-v4.0 across chunk retrieval benchmarks, while removing the 32K-token ceiling through extended context handling via backend partitioning. Enabled by Matryoshka learning and quantization-aware training, voyage-context-4 supports embeddings in 2048, 1024, 512, and 256 dimensions, with multiple quantization options. Learn more about voyage-context-4 here: https://blog.voyageai.com/2026/06/29/voyage-context-4/