New embedding models and API updates

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.

Welcome EmbeddingGemma, Google's new efficient embedding model

Multi-modal Knowledge Preserving Adapter for Embedding Backward Compatibility

Upgrading embedding models typically requires expensive database re-indexing, as new query embeddings are incompatible with existing database embeddings. While Backward Compatible Training (BCT) mitig...

Multi-modal Knowledge Preserving Adapter for Embedding Backward Compatibility

The paper introduces the Multi-modal Knowledge Preserving Adapter (MKP-Adapter), an adapter-only approach that enables backward compatible training for multi-modal large language models without updating the backbone. It employs a multi-level preservation loss to maintain embedding geometry and a focal re-weighting strategy to focus on difficult samples. Experiments show strong backward compatibility across image, text, visual document, and video retrieval tasks with minimal latency overhead.

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.

Getting Started With Embeddings

Deploy Embedding Models with Hugging Face Inference Endpoints

Efficient Code Embeddings from Code Generation Models

arXiv:2508. 21290v2 Announce Type: replace-cross Abstract: jina-code-embeddings is a novel code embedding model suite designed to retrieve code from natural language queries, perform technical question-answering, and identify semantically similar code snippets across programming languages.

Build a Domain-Specific Embedding Model in Under a Day

🪆 Introduction to Matryoshka Embedding Models

XLSCOUT Unveils ParaEmbed 2.0: a Powerful Embedding Model Tailored for Patents and IP with Expert Support from Hugging Face

Recovering Temporal and Geographic Signals from Language Model Embeddings

arXiv:2609.05721v1 Announce Type: new Abstract: Understanding whether language-model embeddings encode structured real-world information is important for both representation analysis and information...