How Google Uses Embedding

This post introduces three architectural patterns designed to integrate Model Context Protocol (MCP) Apps and Agent-to-User Interface (A2UI) to solve the tradeoff between highly custom iframe environments and native, declarative rendering. By combining these approaches, developers can serve native-feeling UIs directly over MCP servers, embed complex and stateful iframe apps securely inside declarative views, or inject generative UI components into legacy systems. Ultimately, these hybrid frameworks empower engineering teams to deliver secure, performant, and brand-consistent agentic user experiences tailored to their specific project constraints.

Google has announced the general availability of Gemini Embedding 2, a unified model that maps text, images, video, audio, and documents into a single semantic space. This model allows developers to process interleaved multimodal inputs in a single request, significantly improving performance for tasks like agentic RAG, visual search, and content moderation. By supporting over 100 languages and offering features like task-specific prefixes and Matryoshka dimensionality reduction, the model provides a highly efficient and accurate foundation for building complex AI agents.

The article provides an in-depth exploration of the EmbeddingGemma architecture, detailing its origins, embedding generation process, and the comprehensive training methodology.

The article discusses the recent enhancements to the Gemini Batch API, which now includes support for the Gemini Embedding model and compatibility with the OpenAI SDK.

This article discusses the integration of Google's EmbeddingGemma model with Google Cloud's Dataflow to create a scalable embedding pipeline for AI applications.

EmbeddingGemma is an innovative open embedding model designed for on-device AI applications, featuring 308 million parameters for efficient performance.

The article discusses the Gemini Embedding text model and its applications in various industries, highlighting its effectiveness in enhancing AI applications through context engineering and retriev...

The article announces the general availability of the Gemini Embedding text model, gemini-embedding-001, in the Gemini API and Vertex AI.

The article discusses the latest updates to the Gemini API, highlighting new models and functionalities that enhance developers' ability to create applications using generative AI.

The article introduces Keras Recommenders, a new library designed to simplify the creation of state-of-the-art recommendation systems using Keras with JAX, TensorFlow, or PyTorch.

The article discusses the new features and improvements in Gemma 3, highlighting its vision-language capabilities, architectural changes for memory efficiency, and enhanced multilingual support.

The article discusses the introduction of the Gemini Embedding text model (gemini-embedding-exp-03-07) available through the Gemini API.

The article discusses the Vertex AI RAG Engine, a tool designed to help developers build grounded generative AI applications by addressing challenges like hallucinations and outdated knowledge.

The article discusses the use of multimodal embeddings to enhance visual search capabilities, particularly for artists and enterprise-scale document search.

The article discusses the PaliGemma architecture, a lightweight open vision-language model (VLM) inspired by PaLI-3.

The article explores the RecurrentGemma architecture, a hybrid model that combines gated linear recurrences with local sliding window attention, enhancing performance for long context prompts.

The article discusses the release of Gemma 2, a new suite of open models that sets a new standard for performance and accessibility in conversational AI.

The article provides an overview of the Gemma model family architectures, detailing its lightweight, state-of-the-art open models derived from Gemini research.

The article introduces the AI Edge Torch Generative API, designed to enable developers to create high-performance LLMs in PyTorch for deployment on edge devices using the TensorFlow Lite runtime.

The article discusses the latest updates from Coral, including a partnership with balena, new open-source tools, and enhancements to their ML software stack.