Embed v4

Embed v4 converts words, sentences, and documents into stable vector representations that capture meaning rather than surface form, so related ideas land close together even when phrased differently or in different languages. It’s tuned for retrieval workflows—clean neighborhood structure for k-NN search, consistent norms for cosine similarity, and predictable behavior across chunk sizes—making it a drop-in backbone for RAG, search, deduplication, topic discovery, and recommendations. The model is optimized for production: batched inference keeps throughput high for indexing jobs, streaming endpoints handle interactive queries with low latency, and quantization options reduce memory without destabilizing similarity scores. Developers typically pair it with a vector database, enforce consistent chunking and normalization, and cache frequent embeddings to control cost. In practice, Embed v4 improves recall and ranking quality out of the box while remaining easy to fine-tune on domain text and glossaries, giving teams a dependable, scalable foundation for semantic retrieval and retrieval-augmented applications.