Jina embeddings v3 now available on Gemini Enterprise Agent Platform Model Garden
Jina search foundation model, jina-embeddings-v3, is now self-deployable on Gemini Enterprise Agent Platform Model Garden, with more to follow. Run jina-embeddings-v3 on a single L4 GPU inside your own VPC.
Unsupervised document clustering with Elasticsearch + Jina embeddings
A practical, reproducible approach to unsupervised document clustering with Elasticsearch and Jina embeddings.
Semantic search, now multilingual by default
semantic_text now defaults to jina-embeddings-v5-text on Elastic Inference Service, enabling multilingual semantic search in Elasticsearch.
Build a "chat with your website data" agent with Jina Embeddings v5 and Elasticsearch
Learn how to build a working, grounded "chat with your website" experience in under an hour by using Elasticsearch Serverless, Jina Embeddings v5, Elastic Open Web Crawler, and Elastic Agent Builder.
jina-embeddings-v5-text: Compact state-of-the-art text embeddings for search and intelligent applications
Introducing jina-embeddings-v5-text models, including jina-embeddings-v5-text-small and jina-embeddings-v5-text-nano, and explaining how to use these multilingual embedding models via Elastic Inference Service (EIS).
jina-vlm: Seeing like an AI with vision language models
Learn about vision language models (VLMs), what jina-vlm can do, how to use it, and best practices.
Jina Rerankers bring fast, multilingual reranking to Elastic Inference Service (EIS)
Elastic now offers jina-reranker-v2-base-multilingual and jina-reranker-v3 on EIS, enabling fast multilingual reranking directly in Elasticsearch for higher-precision retrieval, RAG, and agentic workflows without added infrastructure.
jina-embeddings-v3 is now available on Elastic Inference Service
Introducing jina-embeddings-v3 on Elastic Inference Service (EIS) and explaining how to get started.
An introduction to Jina models, their functionality, and uses in Elasticsearch
Explore Jina multimodal embeddings, Reranker v3, and semantic embedding models, and how to use them natively in Elasticsearch.
NeurIPS 2025 highlights: From model merging to deep learning for code
Explore our NeurIPS 2025 highlights on model merging, task vectors, and VLM dynamics, plus our DL4C workshop presentation on Jina code embeddings.