Tech

Multimodal Embeddings in Llama.cpp and GGUF

We have introduced multiple fixes in our fork of llama.cpp, so that it works with jina-embeddings-v4 on multimodal embeddings.

Jina Code Embeddings: SOTA Code Retrieval at 0.5B and 1.5B

Today we're releasing jina-code-embeddings, a new suite of code embedding models in two sizes—0.5B and 1.5B parameters—along with 1-4 bit GGUF quantizations for both. Built on the latest code generation LLMs, these models achieve state-of-the-art retrieval performance despite their compact size. They support five retrieval tasks including nl2code, code2code, code2nl, code2completions, and qa across 15 programming languages including Python, JavaScript, Java, C++, C#, Go, Rust, TypeScript, SQL, MATLAB, R, Swift, Kotlin, HTML/CSS, PHP, Ruby, Scala, Perl, and Shell.

The Last Days Of Social Media

Social media promised connection, but it has delivered exhaustion.

Agentic Workflow with Jina Remote MCP Server

We showed you, in a previous post, how to integrate Jina AI’s search and reader APIs with DeepSeek R1 to build a deep research agent, but it took a lot of custom code and prompt engineering to make it work. In this post we’ll do the same thing using the Model Context Protocol (MCP), which uses a lot less custom code and is portable to different LLMs, but is still subject to a few pitfalls along the way.

Amazon is betting on agents to win the AI race

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Optimizing GGUFs for Decoder-Only Embedding Models

Two weeks ago, we released GGUF formats of jina-embeddings-v4 - a universal embedding model for multimodal multilingual retrieval - with various quantized versions. Our motivation was simple: as a 3.75B parameter model, the vanilla transformer version of jina-embeddings-v4 doesn't scale well on our GCP G2 (L4 GPU) API instances, so we wanted to speed up inference using these smaller, faster GGUF versions. During our experiments, we discovered some interesting findings while converting and running GGUF embedding models. Since most of the llama.cpp community focuses on LLMs, we thought it'd be valuable to share this from an embedding provider's perspective.

How We Became Captives Of Social Media

Today’s social media has shifted from social networking platforms to AI-enhanced conveyor belts of vapid entertainment. Is there any escape?

What We Learned at SIGIR 2025

SIGIR (Special Interest Group on Information Retrieval) is a top-tier information retrieval conference bringing together researchers, developers, industry experts, and educators from across the globe to share the latest ground-breaking research. Jina AI was at this year’s conference in Padua in July, presenting our work on late chunking at the Robust IR Workshop.

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How Image Resolution Impacts Visual Document Retrieval

Traditional computer vision models typically focus on mimicking human visual perception. jina-embeddings-v4 takes a different approach: it combines image and text processing to understand how people read and interpret information presented visually. Unlike OCR programs that simply digitize text, it actually parses complex visual materials like infographics, charts, diagrams, and tables—documents where both the text and visual elements carry semantic meaning. We call these "visually rich documents."

Errors found in US judge’s withdrawn decision stink of AI

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Amazon buys Bee AI wearable that listens to everything you say

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