Newsroom

Comparing how long-context embedding models perform with different chunking strategies to find the optimal approach for your needs.

You use our embedding models to do what? This might be the most "out-of-domain" applications of embeddings we learned at EMNLP 2024.

Jina-CLIP v2, a 0.9B multimodal embedding model with multilingual support of 89 languages, high image resolution at 512x512, and Matryoshka representations.

Is Meta-Prompt the new norm for API specs? Feed it to LLMs and generate integration code that reliably integrates Jina's APIs, saving you from the usual trial-and-error process.

At EMNLP 2024 Miami? Join us for a Birds of a Feather session focusing on embeddings, rerankers, and small LMs for better search.

Learn how Jina-CLIP enhances OpenAI's CLIP with better retrieval accuracy and more diverse results through unified text-image embeddings.

We trained three small language models to better segment long documents into chunks, and here are the key lessons we learned.

New Classifier API offers zero-shot and few-shot classification for text and images. Start classifying content instantly or train it with your own examples.

With the new g.jina.ai, you can easily ground statements to reduce LLM hallucinations or improve the integrity of human-written content.

Multilingual models often face a "language gap," where similar phrases in different languages don't align. We show how contrastive learning can bridge this gap, enhancing cross-language performance.

Part 2 of our exploration of Late Chunking, a deep dive into why it is the best method for chunk embeddings and improving search/RAG performance.

We collected some tips to help you migrate from Jina Embeddings v2 to v3.