Building with Gemini Embedding 2: Agentic multimodal RAG and beyond

2026-04-30 · Source: Google Developers Blog - AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, medium

Summary

Google announced the General Availability (GA) of Gemini Embedding 2 on April 30, 2026, accessible via the Gemini API and Gemini Enterprise Agent Platform. This is the first embedding model in the Gemini API that unifies text, images, video, audio, and documents into a single embedding space, supporting over 100 languages. The model processes interleaved inputs, such as text and images, in a single request, handling up to 8,192 text tokens, 6 images, 120 seconds of video, 180 seconds of audio, and 6 PDF pages. Key applications include agentic multimodal RAG, multimodal search, search reranking, clustering, classification, and anomaly detection. Users like Harvey and Nuuly have reported significant improvements in precision and accuracy, with Nuuly achieving an 87% Match@20 accuracy for visual search.

Key takeaway

For AI Engineers building multimodal applications, Gemini Embedding 2 offers a unified approach to process diverse data types. You should explore its capabilities for agentic RAG, visual search, and reranking to improve accuracy and efficiency. Consider using task prefixes and Matryoshka Representation Learning for optimized performance and cost-effective storage in vector databases like Pinecone or Weaviate.

Key insights

Gemini Embedding 2 unifies diverse modalities into a single embedding space for enhanced AI understanding and applications.

Principles

• Unified embeddings improve multimodal data understanding.
• Task prefixes optimize embeddings for specific retrieval goals.
• Matryoshka Representation Learning enables efficient vector truncation.

Method

Generate embeddings for multimodal inputs using the Gemini API, optionally applying task-specific prefixes for asymmetric retrieval, then store in vector databases for various AI tasks.

In practice

• Use "task: question answering | query: {content}" for RAG.
• Truncate vectors to 1536 or 768 dimensions for cost savings.
• Employ Batch API for 50% lower embedding prices.

Topics

• Gemini Embedding 2
• Multimodal Embeddings
• Agentic RAG
• Visual Search
• Vector Databases

Code references

• google-gemini/cookbook

Best for: AI Engineer, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Google Developers Blog - AI.