Retrieval-augmented generation

Training and Finetuning Embedding Models with Sentence Transformers

Building Cost-Efficient Enterprise RAG applications with Intel Gaudi 2 and Intel Xeon

Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval

Embedding AI into developer software

JetBrains uses OpenAI’s API to build its fastest-growing product ever.

ScreenAI: A visual language model for UI and visually-situated language understanding

Posted by Srinivas Sunkara and Gilles Baechler, Software Engineers, Google Research Screen user interfaces (UIs) and infographics, such as charts, diagrams and tables, play important roles in human communication and human-machine interaction as they facilitate rich and interactive user experiences. UIs and infographics share similar design principles and visual language (e.

CPU Optimized Embeddings with 🤗 Optimum Intel and fastRAG

Health-specific embedding tools for dermatology and pathology

Posted by Dave Steiner, Clinical Research Scientist, Google Health, and Rory Pilgrim, Product Manager, Google Research There’s a worldwide shortage of access to medical imaging expert interpretation across specialties including radiology , dermatology and pathology . Machine learning (ML) technology can help ease this burden by powering tools that enable doctors to interpret these images more accurately and efficiently.

VideoPrism: A foundational visual encoder for video understanding

Posted by Long Zhao, Senior Research Scientist, and Ting Liu, Senior Staff Software Engineer, Google Research An astounding number of videos are available on the Web, covering a variety of content from everyday moments people share to historical moments to scientific observations, each of which contains a unique record of the world. The right tools could help researchers analyze these videos, transforming how we understand the world around us.

🪆 Introduction to Matryoshka Embedding Models

A decoder-only foundation model for time-series forecasting

Posted by Rajat Sen and Yichen Zhou, Google Research Time-series forecasting is ubiquitous in various domains, such as retail, finance, manufacturing, healthcare and natural sciences. In retail use cases, for example, it has been observed that improving demand forecasting accuracy can meaningfully reduce inventory costs and increase revenue.

Graph neural networks in TensorFlow

Posted by Dustin Zelle, Software Engineer, Google Research, and Arno Eigenwillig, Software Engineer, CoreML Objects and their relationships are ubiquitous in the world around us, and relationships can be as important to understanding an object as its own attributes viewed in isolation — take for example transportation networks, production networks, knowledge graphs, or social networks. Discrete mathematics and computer science have a long history of formalizing such networks as graphs , consisting of nodes connected by edges in various irregular ways.

New embedding models and API updates

Deploy Embedding Models with Hugging Face Inference Endpoints

New and improved embedding model

We are excited to announce a new embedding model which is significantly more capable, cost effective, and simpler to use.

MTEB: Massive Text Embedding Benchmark

Getting Started With Embeddings

Introducing text and code embeddings

We are introducing embeddings, a new endpoint in the OpenAI API that makes it easy to perform natural language and code tasks like semantic search, clustering, topic modeling, and classification.

Text and code embeddings by contrastive pre-training

Train a Sentence Embedding Model with 1B Training Pairs

Retrieval Augmented Generation with Huggingface Transformers and Ray