Build a Multimodal Agentic RAG App with Gemini Embedding 2 and Google ADK

2024-09-14 · Source: unwind ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

This tutorial details the construction of a multimodal agentic RAG application utilizing Gemini Embedding 2 and the Google Agent Development Kit (ADK). The application integrates text, URLs, PDFs, images, audio, and video into a single 768-dimension embedding space, enabling unified retrieval. Gemini Embedding 2 handles the embedding of diverse modalities, employing task prefixes like "task: retrieval document" and "task: question answering | query" to enhance retrieval quality. The Google ADK agent coordinates retrieval and synthesizes grounded, cited answers. Key features include a truly multimodal index, a single retrieval packet consumed by both the agent and the UI for consistent citations, and a 3D PCA embedding view. The backend is implemented with FastAPI, Python 3.12, and requires a Gemini API key.

Key takeaway

For AI Engineers building RAG applications that handle diverse data types, this architecture provides a robust, open-source blueprint. You should consider adopting Gemini Embedding 2 for its multimodal capabilities and Google ADK for agentic coordination to ensure consistent, grounded answers and citations across text, images, audio, and video. This approach simplifies pipeline complexity by unifying embedding spaces and retrieval logic.

Key insights

Gemini Embedding 2 and Google ADK enable unified multimodal RAG with consistent citations across diverse data types.

Principles

• Blend media and text vectors for improved file search.
• Use task prefixes for better embedding retrieval.
• Single retrieval contract ensures citation consistency.

Method

Sources are chunked and embedded with Gemini Embedding 2 using task prefixes. Queries are embedded, and cosine similarity ranks chunks. A Google ADK agent then uses the retrieved context to generate a grounded answer, ensuring the UI and agent share the same retrieval packet.

In practice

• Implement SSRF protection for URL ingestion.
• Offload blocking calls to a threadpool for responsiveness.
• Delete uploaded files via Gemini File API in a finally block.

Topics

• Multimodal RAG
• Gemini Embedding 2
• Google ADK
• ADK Agent
• Multimodal Embeddings

Code references

• Shubhamsaboo/awesome-llm-apps
• Shubhamsaboo/awesome-llm-apps

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

Related on AIssential

• Building with Gemini Embedding 2: Agentic multimodal RAG and beyond — Gemini Embedding 2 unifies diverse modalities into a single embedding space for enhanced AI understanding and applications.
• Gemini Embedding 2 - Multimodal (Text, Images, PDF, Audio, Video) Embeddings for RAGs and Agents — Gemini Embeddings 2 offers unified multimodal embeddings for diverse data types, enhancing semantic search and analysis.
• Gemini API File Search: The Easy Way to Build RAG — Google's File Search tool automates RAG infrastructure, enabling multimodal search across text and images with Gemini API.
• Multimodal RAG + Gemini Embedding 2 + GPT-5.4 Just Revolutionized AI Forever — Gemini Embedding 2 unifies multimodal data into a single vector space, simplifying AI system architecture.
• Google AI Introduces Gemini Embedding 2: A Multimodal Embedding Model that Lets Your Bring Text, Images, Video, Audio, and Docs into the Embedding Space — Gemini Embedding 2 unifies multimodal data into a single latent space, optimizing RAG with flexible vector dimensions.
• Make any media searchable — Multimodal AI and advanced agent capabilities are rapidly expanding the scope of AI applications and development.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by unwind ai.