Multimodal Embeddings and RAG: A Practical Guide | Weaviate

Introduces and details the practical application of multimodal embeddings for semantic search and RAG. Explains how natively multimodal embedding models (like Gemini Embedding 2) map text, images, audio, and video into a shared embedding space. Discusses key design decisions: native vs. bridge-based embedding, chunking strategies for non-text data (fixed-width time windows for audio/video, page-level for documents), and the use of Matryoshka Representation Learning (MRL) for dimension reduction. Provides concrete examples of building multimodal systems with Weaviate, Gemini Embedding 2, and Gemini 3 Flash, including searching audio without transcripts, searching images with text, and multimodal RAG with video.