Choosing RAG or long context depends on dataset size, with RAG suited to dynamic knowledge bases and long context best for bounded files.

Gemini Embedding 2 ships cross-modality retrieval with Matryoshka vectors, offering flexible dimensions for cost and accuracy tradeoffs.

Google unveils Gemini Embedding 2, a multimodal AI model for RAG, semantic search and clustering across 100+ languages.

Elastic 9.3.0 is now available, featuring enhanced vector search indexing for RAG applications and significant upgrades to ...

In a blog post, the tech giant detailed the new AI model. It is the successor to the text-only embedding model that was released last year, and it captures semantic intent across more than 100 ...