#embeddings

Explore the essentials of RAG system architecture, its components, implementation strategies, and best practices to build production-ready systems.

Binary encoding improvements, backup enhancements, and Gemini Embedding 2 audio support arrive in Weaviate v1.36.6. What builders need to know.

Weaviate adds audio support to Gemini Embedding 2 Multimodal, expanding what vectors you can store and search. Replication and backup improvements tighten operations.

Weaviate adds Gemini Embedding 2 audio capabilities to multi2vec-google, expanding multimodal vector search beyond text and images. Replication and backup improvements included.

Audio embedding support lands in Weaviate's Google module. Here's what builders need to do with async replication improvements and backup enhancements.

Voyage AI's new model family shares a single vector space, letting you swap embedding models without rebuilding indexes. Direct impact on production RAG cost and iteration speed.

Voyage AI's Automated Embedding feature is now in public preview, streamlining how developers generate and manage embeddings at scale. Here's what changed and why it matters for your RAG pipeline.

Qdrant now integrates Google's Gemini Embedding 2, enabling multimodal embeddings across text and images. Builders can ship semantic search that understands both modalities without pipeline fragmentation.

Jina's latest embeddings models are integrated into Elastic's inference service. Here's what changed and why it matters for your search and RAG infrastructure.

Google's first multimodal embedding model expands beyond text. What builders need to know about implementation, limitations, and competitive positioning.

Jina's v5 text embeddings are now integrated into Elastic Inference Service. For builders, this means production-ready multilingual embeddings without managing separate inference infrastructure.