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A collection of 111 posts

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Text Embeddings Fail to Capture Word Order and How to Fix It

Text embedding models struggle with capturing subtle linguistic nuances like word order, directional relationships, temporal sequences, causal connections, comparisons, and negation. Understanding these challenges is key to improving model performance.

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Scaling Test-Time Compute For Embedding Models

Better results scale with compute—more on learning, more on search. A good pretrained model takes you far, but test-time compute takes you further. It's time to recognize this paradigm of test-time compute, even for embedding models.

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Still Need Chunking When Long-Context Models Can Do It All?

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

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Watermarking Text with Embedding Models to Protect Against Content Theft

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

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Meta-Prompt for Better Jina API Integration and CodeGen

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.

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Beyond CLIP: How Jina-CLIP Advances Multimodal Search

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

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Finding Optimal Breakpoints in Long Documents Using Small Language Models

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

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Bridging Language Gaps in Multilingual Embeddings via Contrastive Learning

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.

Slide depicting the "Late Chunking" process, with flow charts and a model highlighting the transition from a "Long Document"

What Late Chunking Really Is & What It’s Not: Part II

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.

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Migration From Jina Embeddings v2 to v3

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

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The What and Why of Text-Image Modality Gap in CLIP Models

You can't just use a CLIP model to retrieve text and images and sort the results by score. Why? Because of the modality gap. What is it, and where does it come from?

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Tech Blog Featured

Late Chunking in Long-Context Embedding Models

Chunking long documents while preserving contextual information is challenging. We introduce the "Late Chunking" that leverages long-context embedding models to generate contextual chunk embeddings for better retrieval applications.

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Rephrased Labels Improve Zero-Shot Text Classification by 30%

When using embedding models for zero-shot classification, rephrasing the class label to "This is seriously about 'LABEL'" gives higher accuracy vs. using LABEL alone. But how, and why?

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Can Embedding/Reranker Models Compare Numbers?

A lot of LLMs can't figure out that 9.11 is actually smaller than 9.9. Can our embedding and reranker models do any better?

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No. You Can't Use Reranker to Improve SEO

But if you work in SEO, it could be interesting to see things from the other side of the table; understand how embeddings and rerankers play their roles in modern search systems.

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Handcrafting Image Prompts Is Dead: Reverse Engineer Midjourney-style Images with PromptPerfect

From Punk Einstein to Turbo Pigeons: Use PromptPerfect Interactive to reverse engineer prompts from pictures and generate Midjourney-style images with real-time feedback.

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AI Explainability Made Easy: How Late Interaction Makes Jina-ColBERT Transparent

AI explainability and transparency are hot topics. How can we trust AI if we can't see how it works? Jina-ColBERT shows you how, with the right model architecture, you can easily make your AI spill its secrets.

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Implementing a Chat History RAG with Jina AI and Milvus Lite

Enhance your search applications in Python with Jina Embeddings and Reranker and lightweight, easy-to-deploy Milvus Lite.

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Bypass Limitations with PromptPerfect: Generate the Images the Models Don’t Want You to See

See how PromptPerfect overcomes restrictions and limitations of image generation models like Stable Diffusion XL and DALL-E 3.

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AIR-Bench: Better Metrics for Better Search Foundation

AIR-Bench is a new approach to AI metrics that uses generative AI to make more realistic and flexible benchmarks. With AIR-Bench, you can create your own benchmarks for your own domain, and know that benchmarks data hasn't leaked into model training data.

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Binary Embeddings: All the AI, 3.125% of the Fat

32-bits is a lot of precision for something as robust and inexact as an AI model. So we got rid of 31 of them! Binary embeddings are smaller, faster and highly performant.

Albus by Springworks: Empowering Employees with Enterprise Search

Learn how a leading HR-tech startup uses Jina AI’s models to talk with structured and unstructured data.

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Create Your Personalized Podcast With Jina Reader and PromptPerfect

Use Jina Reader and PromptPerfect to generate your custom news podcast with RSS feeds, article extraction, LLMs, and Text-to-Speech.

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Jina Embeddings and Reranker on Azure: Scalable Business-Ready AI Solutions

Jina Embeddings and Rerankers are now available on Azure Marketplace. Enterprises that prioritize privacy and security can now easily integrate Jina AI's state-of-the-art models right in their existing Azure ecosystem.

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Having It Both Ways: Combining BM25 with AI Reranking

Learn how to integrate Jina Reranker with lexical search engines to take advantage of superior semantic understanding while avoiding the downsides of migrating to a fully-fledged vector search infrastructure.