Tech Blog 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.
Tech Blog 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.
Tech Blog 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.
Tech Blog 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.
Tech Blog Migration From Jina Embeddings v2 to v3 We collected some tips to help you migrate from Jina Embeddings v2 to v3.
Tech Blog 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?
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.
Tech Blog 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?
Tech Blog 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?
Tech Blog 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.
Tech Blog 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.
Tech Blog 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.
Tech Blog 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.
Tech Blog 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.
Tech Blog 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.
Tech Blog 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.
Tech Blog 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.
Tech Blog 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.
Tech Blog 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.
Tech Blog 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.
Tech Blog Retrieve Jira Tickets with Jina Reranker and Haystack 2.0 Learn how to use Jina Reranker and Embeddings with Haystack to create your own Jira ticket search engine, streamlining your operations and never again waste time creating duplicate issues.
Tech Blog Featured DSPy: Not Your Average Prompt Engineering Heads up, Bay Area guys ditched their AVP already and buzz about DSPy now. Could DSPy be the new go-to framework for prompt engineering after LangChain and LlamaIndex?
Tech Blog Next-Level Cloud AI: Jina Embeddings and Rerankers on Amazon SageMaker Learn to use Jina Embeddings and Reranking models in a full-stack AI application on AWS, using only components available in Amazon SageMaker and the AWS Marketplace.
Tech Blog How to Build Article Recommendations with Jina Reranker API Only You can build an article recommendation system with just the Jina Reranker API—no pipeline, no embeddings, no vector search, only reranking. Find out how in 20 lines of code.
Tech Blog Precise RAG with Jina Reranker and LlamaIndex Just Rerank It! Jina Reranker and LlamaIndex take your RAG up to the next level.
