Agentic systems and enterprise search depend on strong data retrieval that works efficiently and accurately. Database provider MongoDB thinks its newest embeddings models help solve falling retrieval quality as more AI systems go into production. As agentic and RAG systems move into production, retrieval quality is emerging as a quiet failure point — one that can undermine accuracy, cost, and user trust even when models themselves perform well. The company launched four new versions of its embeddings and reranking models. Voyage 4 will be available in four modes: voyage-4 embedding, voyage-4-large, voyage-4-lite, and voyage-4-nano.   MongoDB said the voyage-4 embedding serves as its general-purpose model; MongoDB considers Voyage-4-large its flagship model. Voyage-4-lite focuses on tasks requiring little latency and lower costs, and voyage-4-nano is intended for more local development and testing environments or for on-device data retrieval.  Voyage-4-nano is also MongoDB’s first open-weight model. All models are available via an API and on MongoDB’s Atlas platform.  The company said the models outperform similar models from Google and Cohere on the RTEB benchmark. Hugging Face’s RTEB benchmark …