Gemini Embedding 2 - Multimodal (Text, Images, PDF, Audio, Video) Embeddings for RAGs and Agents

2026-03-15 · Source: Venelin Valkov · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

Google has released Gemini Embeddings 2, a natively multimodal model capable of embedding text, PDFs, images, audio, and video files using a single unified model. Available in preview via Google Studio and Vertex AI APIs, it supports text up to 8,000 tokens, up to six images per request, 120 seconds of video, 80 seconds of audio, and six pages of PDF files. The model's default output length is 372, but users can specify lengths up to 768 using Matrioska representation. While text performance shows modest improvement over Gemini Embeddings 1, the model demonstrates significant performance jumps across other modalities, including code understanding, text-to-image, image-to-text, text-to-document, text-to-video, and speech-to-text. It also allows specifying "task types" like retrieval query or retrieval document to optimize embedding accuracy for specific use cases.

Key takeaway

For AI Engineers building multimodal applications, Gemini Embeddings 2 offers a powerful, unified solution for embedding diverse data types. Its improved performance across non-text modalities and task-specific embedding optimization can significantly enhance the accuracy of RAG and semantic search systems. You should explore its preview via Google Studio or Vertex AI APIs to integrate multimodal capabilities into your next-generation AI agents and workflows.

Key insights

Gemini Embeddings 2 offers unified multimodal embeddings for diverse data types, enhancing semantic search and analysis.

Principles

• Multimodal embeddings improve cross-modal understanding.
• Task-specific embeddings optimize accuracy.

Method

Embed content by calling the `embed_content` function with the Gemini embedding module, file bytes, and an `EmbedConfig` specifying the task type (e.g., retrieval document or query).

In practice

• Use for multimodal RAG and semantic search.
• Embed images, audio, and text for similarity searches.
• Specify task types for optimized embeddings.

Topics

• Gemini Embeddings 2
• Multimodal Embeddings
• Semantic Search
• Retrieval-Augmented Generation
• Vertex AI

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Venelin Valkov.