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Vector Search with Gemini Embedding and Open EmbeddingGemma Models

Qdrant - Vector Database & Search Engine via YouTube

Overview

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Explore Google DeepMind's cutting-edge embedding solutions in this 16-minute conference talk that examines the state-of-the-art Gemini Embedding and open EmbeddingGemma models for retrieval applications. Learn about task-type controls and dimensionality options while discovering how to integrate these powerful models into vector search architectures ranging from basic prototypes to optimized production systems. Master essential patterns for prompt-task alignment, make informed indexing decisions that effectively balance accuracy and latency requirements, and understand the various configuration options available for optimizing domain-specific performance. Gain practical insights into where each model excels and develop the confidence to deploy them effectively for advanced retrieval tasks in real-world scenarios.

Syllabus

Vector Search with Gemini Embedding and Open EmbeddingGemma Models | Google DeepMind | Patrick Löber

Taught by

Qdrant - Vector Database & Search Engine

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