Multimodality Embeddings Bilge Yücel DevRel Engineer Stefano Fiorucci AI/Software Engineer Multimodal Search with Gemini Embedding 2 in Haystack Build multimodal search systems in Haystack using Gemini Embedding 2 to embed text, images, video, audio, and PDFs in a shared vector space. March 10, 2026

Multimodal Search with Gemini Embedding 2 in Haystack Build multimodal search systems in Haystack using Gemini Embedding 2 to embed text, images, video, audio, and PDFs in a shared vector space. March 10, 2026Embeddings are the backbone of modern AI applications, from semantic search and recommendation systems to Retrieval-Augmented Generation (RAG). However, most embedding models operate in a single modality, typically focusing only on textual data. Google has introduced Gemini Embedding 2, a fully multimodal embedding model that maps text, images, video, audio, and PDFs into a shared vector space. This means you can search across different types of data using a single embedding model: gemini-embedding-2-preview . Even better, Haystack supports Gemini Embedding 2 from Day 0. Through the Google GenAI x Haystack integration, you can immediately start using the model in your Haystack applications for both text and multimodal…