"MongoDB has recently announced the public preview of its Embedding and Reranking API on MongoDB Atlas. The new API gives developers direct access to Voyage AI's search models within the managed cloud database, enabling them to create features such as semantic search and AI-powered assistants within a single integrated environment, with consolidated monitoring and billing. This option consolidates the components needed to build AI retrieval on a single platform."
"One of the main announcements at the .local San Francisco event, the Voyage 4 series is now available and consists of four different models: voyage-4-large, voyage-4, voyage-4-lite, and the open-weights voyage-4-nano. While previous generations of embedding models required using identical models to embed both queries and documents, Voyage 4 provides text embedding models that work in the same embedding space, so teams can, for example, store data using voyage-4-large and run queries with any Voyage 4 model. Furthermore, automated embedding in vector search is available in preview in the community edition, and Lexical Prefilters for MongoDB Vector Search is in public preview, providing developers with text and geo analysis filters alongside vector search."
MongoDB Atlas offers a public-preview Embedding and Reranking API that exposes Voyage AI search models inside the managed cloud database. The API enables semantic search, retrieval-augmented generation, and AI assistants within a unified environment with consolidated monitoring and billing. The API is database-agnostic and can integrate with any tech stack. The Voyage 4 series includes voyage-4-large, voyage-4, voyage-4-lite, and the open-weights voyage-4-nano models. Voyage 4 embedding models operate in a shared embedding space so stored data and queries can use different Voyage 4 models. Automated embedding and Lexical Prefilters for vector search are available in preview.
Read at InfoQ
Unable to calculate read time
Collection
[
|
...
]